diff --git a/.gitignore b/.gitignore
index b6e4761..eaa7b9e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,5 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+audio/
diff --git a/applications.py b/applications.py
new file mode 100644
index 0000000..f0ead11
--- /dev/null
+++ b/applications.py
@@ -0,0 +1,300 @@
+# Manim Cairo
+from manimlib import *
+# ManimGL
+# from manimlib import *
+import sys
+import os.path
+from manimlib.mobject.svg.drawings import SpeechBubble
+from manimlib.mobject import geometry
+# import six,colour,nncc2gl
+from nn import *
+
+class Application(NeuralNetwork):
+    def construct(self):
+        NeuralNetwork.construct(self)
+        self.play(
+            *[FadeOut(mob)for mob in self.mobjects]
+            # All mobjects in the screen are saved in self.mobjects
+        )
+        NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2]
+        NeuralNetwork.add_neurons(self,False)
+        NeuralNetwork.add_edges(self,False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(0.5)
+        print('...')
+        self.clear()
+        lineoguidance = Line([-3, 0, 0], [3, 0, 0]).rotate(90*DEGREES)
+        self.wait(6)
+        self.v = self.virtual_assistant()
+        self.wait(12.5)
+        grp=self.grp
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 2]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(0.5)
+        v_copy = self.v.copy().move_to(lineoguidance.get_end()).scale(0.18)
+        lineoguidance.rotate(60*DEGREES)
+        self.email = self.mail().move_to(lineoguidance.get_end()).scale(0.25)
+        lineoguidance.rotate(60*DEGREES)
+        self.youtube = self.yt_logo().move_to(lineoguidance.get_end()).scale(0.25)
+        lineoguidance.rotate(60*DEGREES)
+        self.tc = self.textclassification().move_to(lineoguidance.get_end()).scale(0.2)
+        lineoguidance.rotate(60*DEGREES)
+        self.stock_prediction = self.stock_prediction().move_to(
+            lineoguidance.get_end()).scale(0.15)
+        lineoguidance.rotate(60*DEGREES)
+        self.credit_card = self.credit_card().move_to(lineoguidance.get_end()).scale(0.25)
+        # self.rotating_applications()
+        # sun = Circle()
+
+
+        self.play(Transform(self.v, v_copy), FadeIn(self.credit_card), FadeIn(
+            self.email), FadeIn(self.tc), FadeIn(self.stock_prediction), FadeIn(self.youtube),ReplacementTransform(grp,self.grp))
+        self.path = ArcBetweenPoints(start=self.email.get_center(),end=self.youtube.get_center(),radius=3,angle=TAU/6)
+        self.path1 = ArcBetweenPoints(start=self.youtube.get_center(),end=self.tc.get_center(),radius=3,angle=TAU/6)
+        self.path2 = ArcBetweenPoints(start=self.tc.get_center(),end=self.stock_prediction.get_center(),radius=3,angle=TAU/6)
+        self.path3 = ArcBetweenPoints(start=self.stock_prediction.get_center(),end=self.credit_card.get_center(),radius=3,angle=TAU/6)
+        self.path4 = ArcBetweenPoints(start=self.credit_card.get_center(),end=self.v.get_center(),radius=3,angle=TAU/6)
+        self.path5 = ArcBetweenPoints(start=self.v.get_center(),end=self.email.get_center(),radius=3,angle=TAU/6)
+        # path5 = ArcBetweenPoints(start=credit_card.get_center(),end=tc.get_center(),radius=3,angle=TAU/6)
+
+        self.rotating_applications()
+        self.wait(12)
+
+        # self.embed()
+
+    def alt_text(self):
+        self.ttc = Text('Text Classification').scale(0.5)
+        self.tem = Text('Spam Filters').scale(0.5)
+        self.tsp = Text('Stock Prediction').scale(0.5)
+        self.cc = Text('Fraud Detection').scale(0.5)
+        self.yt = Text('Video Recommendation').scale(0.5)
+        self.tv = Text('Virtual Assistant').scale(0.5)
+
+        always(self.ttc.next_to, self.cc_copy, RIGHT)
+        always(self.tem.next_to, self.email_copy, RIGHT)
+        always(self.tsp.next_to , self.stock_prediction_copy, RIGHT)
+        always(self.yt.next_to, self.youtube_copy, RIGHT)
+        always(self.cc.next_to, self.cc_copy, RIGHT)
+        # always(self.tv.next_to, self.v, RIGHT)
+
+    def rotating_applications(self):
+
+        self.cc_copy = self.credit_card.copy().move_to(3*UP).scale(2)
+        self.tc_copy = self.tc.copy().move_to(3*UP).scale(2)
+        self.email_copy = self.email.copy().move_to(3*UP).scale(2)
+        self.stock_prediction_copy = self.stock_prediction.copy().move_to(3*UP).scale(2)
+        self.youtube_copy = self.youtube.copy().move_to(3*UP).scale(2)
+        # cc_copy = self.credit_card.copy().move_to(3*UP).scale(2)
+        self.alt_text()
+
+        self.play(MoveAlongPath(self.email, self.path),
+                    MoveAlongPath(self.youtube, self.path1),
+                    MoveAlongPath(self.tc, self.path2),
+                    MoveAlongPath(self.stock_prediction, self.path3),
+                    MoveAlongPath(self.credit_card, self.path4),
+                    MoveAlongPath(self.v, self.path5),
+                    Transform(self.credit_card,self.cc_copy))
+        self.play(FadeIn(self.cc))
+        self.play(ApplyMethod(self.credit_card.scale,0.5),
+                    MoveAlongPath(self.email, self.path1),
+                    MoveAlongPath(self.youtube, self.path2),
+                    MoveAlongPath(self.tc, self.path3),
+                    MoveAlongPath(self.stock_prediction, self.path4),
+                    MoveAlongPath(self.credit_card, self.path5),
+                    MoveAlongPath(self.v,self.path),
+                    Transform(self.stock_prediction,self.stock_prediction_copy),
+                    ReplacementTransform(self.cc,self.tsp))
+        self.wait()
+        self.play(ApplyMethod(self.stock_prediction.scale,0.5),
+                    MoveAlongPath(self.email, self.path2),
+                    MoveAlongPath(self.youtube, self.path3),
+                    MoveAlongPath(self.tc, self.path4),
+                    MoveAlongPath(self.stock_prediction, self.path5),
+                    MoveAlongPath(self.credit_card, self.path),
+                    MoveAlongPath(self.v, self.path1),
+                    Transform(self.tc,self.tc_copy),
+                    ReplacementTransform(self.tsp,self.ttc))
+        self.wait()
+        self.play(ApplyMethod(self.tc.scale,0.5),
+                    MoveAlongPath(self.email, self.path3),
+                    MoveAlongPath(self.youtube, self.path4),
+                    MoveAlongPath(self.tc, self.path5),
+                    MoveAlongPath(self.stock_prediction, self.path),
+                    MoveAlongPath(self.credit_card, self.path1),
+                    MoveAlongPath(self.v, self.path2),
+                    Transform(self.youtube,self.youtube_copy),
+                    ReplacementTransform(self.ttc,self.yt))
+        self.wait()
+        self.play(ApplyMethod(self.youtube.scale,0.5),
+                    MoveAlongPath(self.email, self.path4),
+                    MoveAlongPath(self.youtube, self.path5),
+                    MoveAlongPath(self.tc, self.path),
+                    MoveAlongPath(self.stock_prediction, self.path1),
+                    MoveAlongPath(self.credit_card, self.path2),
+                    MoveAlongPath(self.v, self.path3),
+                    Transform(self.email, self.email_copy),
+                    ReplacementTransform(self.yt,self.tem))
+        self.wait()
+
+
+    def full_rotate_without_zoom(self,va,v):
+        lineoguidance = Line([-3, 0, 0], [3, 0, 0]).rotate(60*DEGREES)
+        credit_card = self.credit_card().move_to(lineoguidance.get_end()).scale(0.25)
+        lineoguidance.rotate(60*DEGREES)
+        tc = self.textclassification().move_to(lineoguidance.get_end()).scale(0.2)
+        lineoguidance.rotate(120*DEGREES)
+        email = self.mail().move_to(lineoguidance.get_end()).scale(0.25)
+        stock_prediction = self.stock_prediction().move_to(ORIGIN+3*LEFT).scale(0.1)
+        lineoguidance.rotate(60*DEGREES)
+        youtube = self.yt_logo().move_to(lineoguidance.get_end()).scale(0.25)
+        lineoguidance.rotate(60*DEGREES)
+        c = Circle()
+        c1 = Circle()
+        c2 = Circle()
+        c3 = Circle()
+        c4 = Circle()
+        c.move_to(credit_card.get_center())
+        c1.move_to(email.get_center())
+        c2.move_to(tc.get_center())
+        c3.move_to(stock_prediction.get_center())
+        c4.move_to(youtube.get_center())
+        group1 = VGroup(credit_card, c)
+        group2 = VGroup(email, c1)
+        group3 = VGroup(tc, c2)
+        group4 = VGroup(stock_prediction, c3)
+        group5 = VGroup(youtube, c4)
+        path1 = Circle(radius=3).rotate(60*DEGREES)
+        path2 = Circle(radius=3).rotate(120*DEGREES)
+        path3 = Circle(radius=3).rotate(180*DEGREES)
+        path4 = Circle(radius=3).rotate(240*DEGREES)
+        path5 = Circle(radius=3).rotate(300*DEGREES)
+        self.add(group1, group2, group3, group4, group5)
+
+        #path = ArcBetweenPoints(start=tc.get_center(),end=stock_prediction.get_center(),radius=3,angle=TAU/6)
+        # infogroup1 = VGroup(v,infocircle1)
+        self.play(MoveAlongPath(v.infogroup1, v.path), MoveAlongPath(group1, path1), MoveAlongPath(group2, path2), MoveAlongPath(
+            group3, path3), MoveAlongPath(group4, path4), MoveAlongPath(group5, path5), run_time=4, rate_func=linear)
+        # self.remove(v)
+        Arc()
+        # v = self.textclassification()
+        # self.add(v)
+        # credit_card = self.credit_card()
+        # self.play(ShowCreation(credit_card))
+        return None
+
+
+
+    def virtual_assistant(self):
+        r = RoundedRectangle(height=5, width=3).shift(4*RIGHT)
+        self.grp.scale(1.5).next_to(r,LEFT).shift(0.5*LEFT)
+        person = SVGMobject('assets/user(3).svg').to_edge(LEFT)
+        person.set_stroke(width=0.2)
+        bubble = SpeechBubble(height=1.5, width=2.5)
+        bubble.add_content(Text('Hey Google', color=WHITE))
+        # bubble.set_fill(opacity=0)
+        bubble.set_stroke(WHITE, 1)
+        # bubble.pin_to(self)
+        message = Rectangle(height=1, width=2.5).shift(RIGHT)
+        message.move_to(r.get_center()+DOWN)
+        t = Text('Hi, How can I help?').scale(
+            0.25).move_to(message.get_center())
+        g = Text('G').next_to(r.get_corner(RIGHT+UP), LEFT+DOWN)
+        self.play(ShowCreation(r))
+        self.play(Write(person))
+        VGroup(bubble, bubble.content).next_to(
+            person.get_corner(RIGHT+UP), RIGHT)
+        self.play(ShowCreation(bubble), Write(bubble.content))
+        self.wait(2)
+        self.play(ShowCreation(message), Write(t), Write(g))
+        self.wait(10)
+        self.play(Write(self.grp))
+        self.wait()
+        v = VGroup(r, person, bubble, bubble.content, message, t, g)
+        return v
+
+    def mail(self):
+        return SVGMobject('assets/mail.svg')
+
+    def applications_example(self):
+        example1 = Text('Text Recongition')
+        example2 = Text('Image Recongition')
+        example3 = Text('Speech Recongition')
+        example4 = Text('Text Recongition')
+
+    def credit_card(self):
+        card = RoundedRectangle(height=3, width=5)
+        upper_line = Rectangle(height=0.2, width=5).set_fill(WHITE, 1)
+        upper_line.shift(0.7*UP)
+        line1 = Line(stroke_width=10).set_fill(WHITE, 1)
+        line2 = Line(stroke_width=10).set_fill(WHITE, 1)
+        line3 = Line(stroke_width=10).set_fill(WHITE, 1)
+        line4 = Line(stroke_width=10).set_fill(WHITE, 1)
+        line1.set_length(0.8)
+        line2.set_length(0.8)
+        line3.set_length(0.8)
+        line4.set_length(0.8)
+        line_bottom = Line(stroke_width=10)
+        line1.shift(1.8*LEFT)
+        line2.shift(1.8*RIGHT)
+        line3.shift(0.6*RIGHT)
+        line4.shift(0.6*LEFT)
+        line_bottom.move_to(card.get_center()+0.15*BOTTOM+1.2*LEFT)
+        credit_card = VGroup(card, upper_line, line1,
+                             line2, line3, line4, line_bottom)
+        return credit_card
+
+    def yt_logo(self):
+        t = Triangle().set_fill(WHITE, 1)
+        t.rotate(270*DEGREES, about_point=np.array([1, 1, 0]))
+        t.move_to(ORIGIN)
+        r = RoundedRectangle(height=3, width=5)
+        r.set_fill(RED, 1)
+        r.set_stroke(RED,1)
+        t.set_fill(WHITE,1)
+        real_yt_logo = VGroup(r,t)
+        return real_yt_logo
+
+        # self.embed()
+
+    def textclassification(self):
+        box = Rectangle(height=5).shift(LEFT)
+        t = Text('Article').scale(0.6).shift(LEFT)
+        box.surround(t)
+        pointer1 = CurvedArrow(ORIGIN,UP+2*RIGHT,angle=-TAU/4)
+        pointer2 = Arrow(ORIGIN,2*RIGHT)
+        pointer3 = CurvedArrow(ORIGIN,DOWN+2*RIGHT)
+        t1 = Text('Science').scale(0.6).shift(UP+3.5*RIGHT)
+        t2 = Text('Buisness').scale(0.6).shift(3.5*RIGHT)
+        t3 = Text('Entertainment').scale(0.6).shift(DOWN+4*RIGHT)
+        tc = VGroup(box,t,pointer1,pointer2,pointer3,t1,t2,t3)
+        return tc
+
+    def stock_prediction(self):
+        #random values here
+        x = [1, 3, 5, 7, 9, 11]
+        y = [3, 6, 1, 7, 4, 9]
+        axes = Axes((0,12),(0,12))
+        dot_collection = VGroup()
+        for time, dat in zip(x,y):
+            dot = Dot().move_to(axes.coords_to_point(time , dat))
+            dot_collection.add(dot)
+        line_collection = VGroup()
+        for i in range(5):
+            line = Line(dot_collection[i].get_center(),dot_collection[i+1].get_center())
+            line_collection.add(line)
+
+        return VGroup(axes,dot_collection,line_collection)
+
+    def title(self):
+        return
+
+'''
+1. Text classificaon /
+2. Email spam filters /
+3. Speech Recognition /
+4. Recommender System /
+5. Fraud Detection /
+6. Virtual Assistant /
+'''
diff --git a/assets/brain.svg b/assets/brain.svg
new file mode 100644
index 0000000..872572e
--- /dev/null
+++ b/assets/brain.svg
@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Svg Vector Icons : http://www.onlinewebfonts.com/icon -->
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg version="1.1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px" viewBox="0 0 1000 1000" enable-background="new 0 0 1000 1000" xml:space="preserve">
+<metadata> Svg Vector Icons : http://www.onlinewebfonts.com/icon </metadata>
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+       id="path4014"
+       inkscape:connector-curvature="0" />
+  </g>
+  <metadata
+     id="metadata5">
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+      <cc:Work>
+        <dc:format>image/svg+xml</dc:format>
+        <dc:type
+           rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
+        <cc:license
+           rdf:resource="http://creativecommons.org/licenses/publicdomain/" />
+        <dc:publisher>
+          <cc:Agent
+             rdf:about="http://openclipart.org/">
+            <dc:title>Openclipart</dc:title>
+          </cc:Agent>
+        </dc:publisher>
+      </cc:Work>
+      <cc:License
+         rdf:about="http://creativecommons.org/licenses/publicdomain/">
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#Reproduction" />
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#Distribution" />
+        <cc:permits
+           rdf:resource="http://creativecommons.org/ns#DerivativeWorks" />
+      </cc:License>
+    </rdf:RDF>
+  </metadata>
+</svg>
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diff --git a/assets/svg_images/mail.svg b/assets/svg_images/mail.svg
new file mode 100644
index 0000000..1d24d48
--- /dev/null
+++ b/assets/svg_images/mail.svg
@@ -0,0 +1,46 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M485.743,85.333H26.257C11.815,85.333,0,97.148,0,111.589V400.41c0,14.44,11.815,26.257,26.257,26.257h459.487
+			c14.44,0,26.257-11.815,26.257-26.257V111.589C512,97.148,500.185,85.333,485.743,85.333z M475.89,105.024L271.104,258.626
+			c-3.682,2.802-9.334,4.555-15.105,4.529c-5.77,0.026-11.421-1.727-15.104-4.529L36.109,105.024H475.89z M366.5,268.761
+			l111.59,137.847c0.112,0.138,0.249,0.243,0.368,0.368H33.542c0.118-0.131,0.256-0.23,0.368-0.368L145.5,268.761
+			c3.419-4.227,2.771-10.424-1.464-13.851c-4.227-3.419-10.424-2.771-13.844,1.457l-110.5,136.501V117.332l209.394,157.046
+			c7.871,5.862,17.447,8.442,26.912,8.468c9.452-0.02,19.036-2.6,26.912-8.468l209.394-157.046v275.534L381.807,256.367
+			c-3.42-4.227-9.623-4.877-13.844-1.457C363.729,258.329,363.079,264.534,366.5,268.761z"/>
+	</g>
+</g>
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+</svg>
diff --git a/assets/user.svg b/assets/user.svg
new file mode 100644
index 0000000..f7496a3
--- /dev/null
+++ b/assets/user.svg
@@ -0,0 +1,44 @@
+<?xml version="1.0" encoding="iso-8859-1"?>
+<!-- Generator: Adobe Illustrator 19.0.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Capa_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 512 512" style="enable-background:new 0 0 512 512;" xml:space="preserve">
+<g>
+	<g>
+		<path d="M437.02,330.98c-27.883-27.882-61.071-48.523-97.281-61.018C378.521,243.251,404,198.548,404,148
+			C404,66.393,337.607,0,256,0S108,66.393,108,148c0,50.548,25.479,95.251,64.262,121.962
+			c-36.21,12.495-69.398,33.136-97.281,61.018C26.629,379.333,0,443.62,0,512h40c0-119.103,96.897-216,216-216s216,96.897,216,216
+			h40C512,443.62,485.371,379.333,437.02,330.98z M256,256c-59.551,0-108-48.448-108-108S196.449,40,256,40
+			c59.551,0,108,48.448,108,108S315.551,256,256,256z"/>
+	</g>
+</g>
+<g>
+</g>
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+<g>
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+<g>
+</g>
+<g>
+</g>
+</svg>
diff --git a/assets/vector_images/Bubbles_speech.svg b/assets/vector_images/Bubbles_speech.svg
new file mode 100644
index 0000000..d2b659c
--- /dev/null
+++ b/assets/vector_images/Bubbles_speech.svg
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 19.1.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 480 480" style="enable-background:new 0 0 480 480;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#FFFFFF;stroke:#000000;stroke-miterlimit:10;}
+	.st1{fill:none;stroke:#000000;stroke-miterlimit:10;}
+</style>
+<path class="st1" d="M220.1,222.2l-93.9,73.6l127.4-70.5c3.4,0.1,6.8,0.2,10.2,0.2c90.3,0,163.5-40.5,163.5-90.4
+	s-73.2-90.4-163.5-90.4S100.3,85.1,100.3,135C100.3,176.6,151,211.6,220.1,222.2L220.1,222.2z"/>
+</svg>
\ No newline at end of file
diff --git a/bias_update.py b/bias_update.py
new file mode 100644
index 0000000..4aebd93
--- /dev/null
+++ b/bias_update.py
@@ -0,0 +1,96 @@
+from manimlib import *
+from nn import NeuralNetwork
+from applications import Application
+from chooose import Choose
+
+
+class Bias(Choose):
+    def construct(self):
+        axes = Axes((-10, 10), (0, 1.2)).scale(0.5)
+        axes.add_coordinate_labels(
+            x_values=[-10, -5, 0, 5, 10], y_values=[], font_size=15)
+        sigmoid_w0 = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-0.5*x)),
+            use_smoothing=False,
+            color=RED,
+        )
+        sigmoid_w1 = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-1*x)),
+            use_smoothing=False,
+            color=GREEN,
+        )
+        sigmoid_w2 = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-5*x)),
+            use_smoothing=False,
+            color=BLUE,
+        )
+        non_linear_label = Tex(
+            "\\sigma (0.5*x)", color=RED).move_to([4.9, 2, 0])
+        non_linear_label_1 = Tex(
+            "\\sigma (1*x)", color=GREEN).move_to([4.8, 1, 0])
+        non_linear_label_2 = Tex(
+            "\\sigma (5*x)", color=BLUE).move_to([4.8, 0, 0])
+
+        lbl_3 = axes.x_axis.get_number_mobject(3, font_size=15)
+        lbl_negative_3 = axes.x_axis.get_number_mobject(-3, font_size=15)
+        sigma = Tex('\sigma').scale(2)
+        self.wait(3)
+        self.play(Write(sigma))
+        self.play(sigma.animate.shift(1.5*LEFT),run_time=2)
+        self.play(sigma.animate.shift(3*RIGHT),run_time=2)
+        self.wait(2)
+        self.play(FadeOut(sigma))
+        self.play(ShowCreation(axes),run_time=2)
+        self.play(ShowCreation(sigmoid_w1))
+        self.wait()
+        self.play(Write(non_linear_label_1))
+        self.wait()
+        self.play(ReplacementTransform(sigmoid_w1.copy(),
+                  sigmoid_w0), Write(non_linear_label),run_time=2)
+        self.wait()
+        self.play(ReplacementTransform(sigmoid_w0.copy(),
+                  sigmoid_w2), Write(non_linear_label_2),run_time=2)
+        self.wait()
+        sigmoid_w2_copy = sigmoid_w2.copy()
+        self.play(ReplacementTransform(sigmoid_w1.copy(),sigmoid_w2_copy,run_time=3),ZoomInThenZoomOut(non_linear_label_2))
+        self.wait(5.5)
+        self.play(IndicateThenFadeOut(lbl_3, scale_factor=1.5),
+                  FadeOut(sigmoid_w2_copy))
+        self.wait(2.5)
+        self.play(IndicateThenFadeOut(lbl_negative_3, scale_factor=1.5))
+        # self.play(FadeOut(lbl_3))
+        self.wait()
+        self.play(ReplacementTransform(sigmoid_w2, sigmoid_w1.copy()), ReplacementTransform(
+            sigmoid_w0, sigmoid_w1.copy()), FadeOut(VGroup(non_linear_label, non_linear_label_2)))
+        self.wait(2)
+
+        sigmoid_graph_3 = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-(-6+1*x))),
+            use_smoothing=False,
+            color=RED,
+        )
+        # v_line = always_redraw(lambda: axes.get_v_line(dot.get_bottom()))
+        dot = Dot().move_to(axes.i2gp(3, sigmoid_graph_3))
+        line = Line(dot, axes.c2p(3, 0))
+        sigmoid_graph2 = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-(6+1*x))),
+            use_smoothing=False,
+            color=BLUE,
+        )
+        dot1 = Dot().move_to(axes.i2gp(-3, sigmoid_graph2))
+        line1 = axes.get_v_line(dot1.get_bottom())
+
+        bias_label = Tex("\\sigma (1*x+(-6*1))",
+                         color=RED).move_to([4.9, 2, 0])
+        # bias_label_1 = Tex("\\sigma (1*x)", color=GREEN).move_to([4.8,1,0])
+        bias_label_2 = Tex("\\sigma (1*x+(6*1))",
+                           color=BLUE).move_to([4.8, 0, 0])
+
+        # line = axes.get_graph(lambda x: 0.5, color=YELLOW)
+        self.play(ReplacementTransform(sigmoid_w1.copy(),
+                  sigmoid_graph2), Write(bias_label_2))
+        self.play(GrowFromPoint(dot1, dot1.get_center()), ShowCreation(line1))
+        self.play(ReplacementTransform(sigmoid_w1.copy(),
+                  sigmoid_graph_3), Write(bias_label))
+        self.play(ShowCreation(VGroup(dot, line)))
+        # self.embed()
diff --git a/chooose.py b/chooose.py
new file mode 100644
index 0000000..7af6c8d
--- /dev/null
+++ b/chooose.py
@@ -0,0 +1,73 @@
+from numpy import exp
+from manimlib import *
+from nn import *
+from applications import *
+
+
+class Choose(Application):
+
+    def construct(self):
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.2).to_edge(RIGHT).shift(LEFT)
+        title = TexText('Activation Function').move_to([0, 2.5, 0])
+        hidden_layer = TexText('Hidden Layer').move_to([-3, 1, 0])
+        output_layer = TexText('Output Layer').move_to([3, 1, 0])
+        b_classification = TexText(
+            'Binary\\\Classification').move_to([1.5, -1, 0])
+        m_classification = TexText(
+            'Multiclass\\\Classification').move_to([4.5, -1, 0])
+        ReLU = TexText('ReLU\\\Activation').move_to([-3, -1, 0])
+        r = Rectangle().surround(ReLU)
+        sigmoid = TexText('Sigmoid/Tanh\\\Activation').move_to([1.5, -3, 0])
+        softmax = TexText('Softmax\\\Activation').move_to([4.5, -3, 0])
+        line = Arrow(title, hidden_layer)
+        line1 = Arrow(title, output_layer)
+        line2 = Arrow(output_layer, b_classification)
+        line3 = Arrow(output_layer, m_classification)
+        arrow = Arrow(hidden_layer, ReLU)
+        arrow1 = Arrow(b_classification, sigmoid)
+        arrow2 = Arrow(m_classification, softmax)
+        self.wait(2)
+        self.play(Write(title))
+        self.wait(2)
+        self.play(ShowCreation(line),  ShowCreation(line1))
+        self.wait(9)
+        self.play(Write(ReLU))
+        self.wait()
+        self.play(ShowCreation(arrow))
+        self.play(Write(hidden_layer))
+        self.play(Write(b_classification))
+        self.wait()
+        self.play(ShowCreation(arrow1))
+        self.play(Write(sigmoid))
+        self.wait()
+        self.play(Write(output_layer))
+        self.play(ShowCreation(line2))
+        self.play(ShowCreation(line3))
+        self.wait()
+        self.play(Write(softmax))
+        self.wait()
+        self.play(Write(m_classification))
+        self.play(ShowCreation(arrow2))
+        self.wait()
+
+    def label(self, l, group, layer=0, edges=False, bias=False):
+        if bias is True:
+            for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1):
+                label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8)
+                label.move_to(self.bias_layer[x])
+                group.add(label)
+        else:
+            for x in range(NeuralNetwork.arguments['layer_sizes'][layer]):
+                label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8)
+
+                label.move_to(self.grp.edges[layer][x]).shift(
+                    0.4*UP) if edges is True else label.move_to(self.grp.layers[layer][0][x])
+                group.add(label)
+
+        return group
+
+#TODO softmax function
diff --git a/data/activation1.csv b/data/activation1.csv
new file mode 100644
index 0000000..be67031
--- /dev/null
+++ b/data/activation1.csv
@@ -0,0 +1,13 @@
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diff --git a/data/activation2.csv b/data/activation2.csv
new file mode 100644
index 0000000..d40c633
--- /dev/null
+++ b/data/activation2.csv
@@ -0,0 +1,11 @@
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diff --git a/data/activation3.csv b/data/activation3.csv
new file mode 100644
index 0000000..ad1da93
--- /dev/null
+++ b/data/activation3.csv
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diff --git a/data/activation4.csv b/data/activation4.csv
new file mode 100644
index 0000000..cb8449a
--- /dev/null
+++ b/data/activation4.csv
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index 0000000..17ec855
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diff --git a/data/test.csv b/data/test.csv
new file mode 100644
index 0000000..966356e
--- /dev/null
+++ b/data/test.csv
@@ -0,0 +1,419 @@
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diff --git a/data/train.csv b/data/train.csv
new file mode 100644
index 0000000..d1d9b77
--- /dev/null
+++ b/data/train.csv
@@ -0,0 +1,892 @@
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diff --git a/data/weights.pkl b/data/weights.pkl
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index 0000000..d2a7c01
Binary files /dev/null and b/data/weights.pkl differ
diff --git a/data/without_training/activation1.csv b/data/without_training/activation1.csv
new file mode 100644
index 0000000..2646820
--- /dev/null
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diff --git a/data/without_training/activation2.csv b/data/without_training/activation2.csv
new file mode 100644
index 0000000..6c389c7
--- /dev/null
+++ b/data/without_training/activation2.csv
@@ -0,0 +1,11 @@
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diff --git a/data/without_training/activation3.csv b/data/without_training/activation3.csv
new file mode 100644
index 0000000..d384f68
--- /dev/null
+++ b/data/without_training/activation3.csv
@@ -0,0 +1,9 @@
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diff --git a/data/without_training/activation4.csv b/data/without_training/activation4.csv
new file mode 100644
index 0000000..b20516e
--- /dev/null
+++ b/data/without_training/activation4.csv
@@ -0,0 +1,7 @@
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diff --git a/data/without_training/y.csv b/data/without_training/y.csv
new file mode 100644
index 0000000..0b0a34c
--- /dev/null
+++ b/data/without_training/y.csv
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diff --git a/data/without_training/y_hat.csv b/data/without_training/y_hat.csv
new file mode 100644
index 0000000..f4c303a
--- /dev/null
+++ b/data/without_training/y_hat.csv
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diff --git a/endscreen.py b/endscreen.py
new file mode 100644
index 0000000..613d89b
--- /dev/null
+++ b/endscreen.py
@@ -0,0 +1,72 @@
+from manimlib import *
+from nn import *
+# from backward_teaser import *
+from graph_scene import *
+from bias_update import *
+class EndScreen(Bias,graph_scene):
+    def construct(self):
+        self.title =  TexText('Thanks for watching!').move_to([-4,3,0])
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6,1]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.2)
+        subscribe = TexText('SUBSCRIBE!').scale(0.8)
+        subs_button = RoundedRectangle(height=1,width=3).set_fill(RED,1).set_stroke(RED,1)
+        like = SVGMobject('assets/like.svg').scale(0.3).next_to(subs_button,LEFT)
+        like.flip(LEFT)
+        subs = VGroup(subs_button,subscribe,like).scale(0.8)
+        subs.to_edge(LEFT)
+        subscribed = TexText('SUBSCRIBED').scale(0.8)
+        subbed_button = RoundedRectangle(
+            height=1, width=3).set_fill(GREY, 1).set_stroke(GREY, 1)
+        liked = SVGMobject(
+            'assets/liked.svg').scale(0.3).next_to(subbed_button,LEFT).set_color(BLUE)
+        liked.flip(LEFT)
+        subbed = VGroup(subbed_button, subscribed,liked).scale(0.8).to_edge(RIGHT)
+
+        x = [[random.uniform(0, 1) for x1 in range(6)], [
+                random.uniform(0, 1) for x1 in range(6)]]
+        self.play(Transform(self.title,TexText('Thanks for watching!').to_edge(UP+LEFT)))
+        self.wait(7)
+        comments = TexText('Let me know in the comment section!')
+        self.play(Write(comments))
+        self.play(FadeOut(comments))
+        self.play(GrowFromCenter(self.grp))
+        self.play(Write(subs))
+        dot = self.grp.layers[0].copy()
+        # dot.set_stroke(BLACK,1)
+        S = TexText('S').move_to(self.grp.layers[0][0][0]).scale(0.5)
+        U = TexText('U').move_to(self.grp.layers[0][0][1]).scale(0.5)
+        B = TexText('B').move_to(self.grp.layers[0][0][2]).scale(0.5)
+        S_END = TexText('S').move_to(self.grp.layers[0][0][3]).scale(0.5)
+        subs_word = VGroup(S,U,B,S_END)
+        self.play(Transform(subs,subs_word))
+        self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill,WHITE, float(x[0][x1])) for x1 in range(6)], *[ApplyMethod(self.grp.layers[2][0][x1].set_fill, WHITE, float(x[1][x1])) for x1 in range(6)])
+        self.grp.layers[3][0].animate.set_fill(WHITE,1)
+        self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill,WHITE,1))
+        self.play(Write(subbed))
+        self.wait(2)
+        grp_fade = VGroup()
+        for mob in self.mobjects:
+            grp_fade.add(mob) if mob is not self.title else None
+        self.play(
+            FadeOut(grp_fade)
+        )
+        subscribe = TexText('Subscribe!').move_to([-4,-1,0])
+        series = TexText('Neural Network\\\ Crash Course Series').move_to([3,-1.5,0])
+        self.play(Write(VGroup(subscribe,series)))
+        self.wait(13)
+
+
+
+
+
+        # self.embed()
+        # self.wait(30)
+
+    def dot(self):
+        dot_layer = VGroup(*[Dot().move_to(self.grp.layers[0][0][i].get_center())
+                           for i in range(NeuralNetwork.arguments['layer_sizes'][0])])
+        dot_layer.set_fill(WHITE, 0)
+        return dot_layer
diff --git a/explanationall.py b/explanationall.py
new file mode 100644
index 0000000..7fabb23
--- /dev/null
+++ b/explanationall.py
@@ -0,0 +1,796 @@
+# Manim Cairo
+from manimlib import *
+# ManimGL
+# from manimlib import *
+import sys
+import matplotlib.pyplot as plt
+import os.path
+from manimlib.mobject.svg.drawings import SpeechBubble
+from manimlib.mobject import geometry
+# import six,colour,nncc2gl
+from nn import *
+from applications import *
+from bias_update import *
+from backward_teaser import *
+from graph1 import *
+from endscreen import *
+class Explanationall(EndScreen):
+    def construct(self):
+        # self.title = TexText('Test').to_edge(UP+LEFT)
+        NeuralNetwork.construct(self)
+        self.fade_all_except_title()
+        Application.construct(self)
+        self.fade_all_except_title()
+        self.wait(2)
+        NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2]
+        NeuralNetwork.add_neurons(self,False)
+        NeuralNetwork.add_edges(self,False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.5)
+        self.edges=self.grp.edges.copy().set_color(YELLOW)
+        self.first_part()
+        self.play(self.camera.frame.animate.scale(10).move_to(ORIGIN),FadeIn(self.grp.edges),FadeOut(VGroup(self.decimal)))
+        self.play(Indicate(self.grp.layers[0],color=WHITE),run_time=3)
+        self.wait(3)
+        # self.play(ApplyMethod(self.camera.frame.move_to,ORIGIN))
+        # self.add(self.grp.scale(1.5))
+
+        self.edge_transform_1 = VGroup()
+        self.connections(0, 0, 6,wait=4, highlight=True)
+        self.play(Indicate(self.grp.layers[2],color=WHITE))
+        self.wait(2)
+        self.play(FadeOut(self.grp))
+        self.weights()
+        # self.wait()
+
+        # self.shallow_nn_representation()
+        # self.grp.scale(1.5)
+        NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2]
+        NeuralNetwork.add_neurons(self,False)
+        NeuralNetwork.add_edges(self,False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.5)
+        bias=TexText('What is bias?').to_corner(UP+LEFT)
+        input_label = self.label("x",VGroup())
+        self.play(Write(VGroup(self.grp)),Transform(self.title,bias))
+
+
+        self.bias_layer = VGroup(*[
+            Circle(
+                radius=NeuralNetwork.arguments["neuron_radius"],
+                stroke_color=NeuralNetwork.arguments["neuron_color"],
+                stroke_width=NeuralNetwork.arguments["neuron_width"],
+                fill_color=NeuralNetwork.arguments["neuron_fill_color"],
+                fill_opacity=NeuralNetwork.arguments["neuron_fill_opacity"],
+            ).scale(1.5).next_to(self.grp.layers[x][0][0],UP)
+            for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1)
+
+            ]
+        )
+        bias_label = self.label("b",VGroup(),bias=True)
+        self.play(GrowFromPoint(self.bias_layer,self.bias_layer.get_center()),Write(bias_label))
+        bias_edges = VGroup()
+        x=0
+        for l1, l2 in zip(self.grp.layers[:-1], self.grp.layers[1:]):
+            edge_group = VGroup()
+
+            y=0
+            for n2 in (l2.neurons):
+                # print(x,y)
+                if y < self.arguments['layer_sizes'][x+1] and x is not len(self.arguments['layer_sizes'])-1:edge_group.add(self.get_edge(self.bias_layer[x], n2, False))
+                else:break
+                y+=1
+
+
+            # edge_group = self.set_bias(x,edge_group) if x is not len(NeuralNetwork.arguments['layer_sizes'])-2 else None
+            self.play(Write(edge_group),
+                      run_time=0.5)
+            bias_edges.add(edge_group)
+            x+=1
+        self.wait(3)
+        self.play(self.bias_layer.animate.set_fill(WHITE,1))
+        self.wait(5)
+        input_grp = VGroup()
+        for x in range(NeuralNetwork.arguments['layer_sizes'][0]):
+            label = Tex('0')
+            label.move_to(self.grp.layers[0][0][x])
+            input_grp.add(label)
+        self.play(Write(input_grp))
+        self.wait(10.5)
+
+        grp = VGroup(self.grp,self.bias_layer,bias_edges,bias_label)
+        self.play(grp.animate.shift(DOWN),input_grp.animate.shift(DOWN))
+        input_label.shift(DOWN)
+        v = VGroup(input_label,bias_label[0]).copy()
+        forward_propagation = Tex("{{w_0\cdot x_1}}+{{w_1\cdot x_2}}+{{...}}+{{w_3\cdot x_4+b_0}}").shift(2.5*UP)
+        z_forward_propagation = Tex(
+            '\sigma({{w_0\cdot x_1}}+{{w_1\cdot x_2}}+{{...}}+{{w_3\cdot x_4+b_0}})').shift(2.5*UP)
+        self.play(ReplacementTransform(input_grp, input_label))
+        self.wait()
+        without_z = Tex("\sum_{i=1}^nw_ix_i+b").shift(3*UP)
+        z = Tex('z = ').next_to(without_z,LEFT)
+        equal = Tex("=").next_to(without_z,RIGHT)
+        inf = Tex("\infty").next_to(equal,RIGHT)
+        negativeinf = Tex("-\infty").next_to(equal,RIGHT)
+        how = TexText('How?').next_to(inf,RIGHT).shift(RIGHT)
+        self.play(ReplacementTransform(v,forward_propagation))
+        self.wait(2)
+        self.play(Transform(forward_propagation,z_forward_propagation))
+        self.wait(5)
+        self.play(ReplacementTransform(forward_propagation,without_z))
+        self.wait(7)
+        self.play(Write(z))
+        self.wait(2)
+        self.play(FadeIn(VGroup(equal,negativeinf)))
+        self.wait()
+        self.play(ReplacementTransform(negativeinf,inf))
+        self.wait(5)
+        # #TODO: activation function: z=wx+b, the values can go up to -inf, +inf, it knows no bounds. so how does the neural network determine which neuron is activated? Thats where activation functions comes in.
+        self.play(FadeOut(VGroup(inf,self.grp,without_z,z,equal,input_label,bias_edges,bias_label,self.bias_layer)))
+        # self.embed()
+        self.wait()
+        af = TexText('What is activation function?').to_corner(UP+LEFT)
+        a = TexText('Activation Function').shift(3*LEFT)
+        arrow = Arrow([0,0,0],[1,0,0]).next_to(a,RIGHT)
+        ans = TexText('Output of a neuron').next_to(arrow,RIGHT)
+        self.play(Write(a), Transform(self.title, af))
+        self.play(Write(arrow))
+        self.play(Write(ans))
+        v = VGroup(a, arrow, ans)
+        self.play(v.animate.shift(2.5*UP))
+        self.wait()
+        circle = Circle(radius=1.5,color=BLUE).shift(DOWN)
+        r_v = ValueTracker(0)
+        restricted_value = DecimalNumber(0, num_decimal_places=3, include_sign=False, unit=None)
+        restricted_value.add_updater(lambda d: d.set_value(r_v.get_value()))
+        restricted_value.move_to(circle.get_center())
+        limit = TexText('Limit: -1 to 1').move_to([-2.5,1,0])
+
+        self.play(ShowCreation(circle),FadeIn(restricted_value),Write(limit))
+        self.play(r_v.set_value, -1,run_time=2.5)
+        self.wait()
+        self.play(r_v.set_value, 1,run_time=2.5)
+        # self.wait(2)
+        print('--')
+        self.play(FadeOut(VGroup(circle,restricted_value,limit)))
+        self.remove(r_v)
+
+        axes = Axes((-5,5),(0,1)).scale(0.5)
+        sigmoid_graph = axes.get_graph(
+                    lambda x: 1.0 / (1.0 + math.exp(-x)),
+                    use_smoothing=False,
+                    color=YELLOW,
+                    )
+        self.play(Write(axes, lag_ratio=0.01, run_time=1))
+        non_linear_label = axes.get_graph_label(sigmoid_graph, Text("Non Linear Function").scale(0.4))
+        self.play(
+            ShowCreation(sigmoid_graph),
+            FadeIn(non_linear_label, RIGHT),
+        )
+        axes1 = Axes(x_range=[-1, 5],
+                     y_range=[-1, 5],width=10).scale(0.4).to_edge(UP+RIGHT)
+        linear_graph = axes1.get_graph(lambda x: x,
+                               x_range=[-1, 4], color=YELLOW)
+        linear_label = axes1.get_graph_label(
+            linear_graph, Text("Linear Function").scale(0.3))
+        self.wait(10)
+        self.play(FadeOut(VGroup(sigmoid_graph,non_linear_label,axes,v)))
+        grp.shift(0.8*UP+0.5*LEFT)
+        cat = ImageMobject('assets/cat.png').next_to(self.grp.layers[0],LEFT).scale(0.5)
+        self.play(Write(self.grp),
+                    FadeIn(cat),
+                    Write(axes1, lag_ratio=0.01, run_time=1),
+                    ShowCreation(linear_graph),
+                    FadeIn(linear_label, RIGHT),run_time=3
+        )
+        soundwave = SVGMobject(
+            'assets/soundwavee.svg').scale(0.5).next_to(self.grp.layers[0], LEFT)
+
+        cross = VGroup(Line([3,2,0],[-3,-2,0],color=RED),Line([-3,2,0],[3,-2,0],color=RED)).move_to(self.grp.get_center())
+        self.play(FadeTransform(cat,self.dot()),ShowCreation(cross),run_time=2)
+        self.wait(5)
+        self.play(FadeIn(soundwave))
+        self.play(FadeTransform(
+            soundwave, self.dot()), ShowCreation(cross),run_time=2)
+        self.wait(5)
+        self.play(
+            *[FadeOut(mob)for mob in self.mobjects]
+            # All mobjects in the screen are saved in self.mobjects
+        )
+        self.title = TexText('Types of Activation Function').to_edge(UP+LEFT)
+        self.play(Write(self.title))
+        graph_scene.construct(self)
+        self.wait()
+        self.fade_all_except_title()
+        axes = Axes((-5, 5), (0, 1.2),
+                    y_axis_config={
+                        "decimal_number_config": {
+                            "num_decimal_places": 1,
+                        }
+                    },
+                    width=10).scale(0.6).shift(0.5*UP)
+        sigmoid_graph = axes.get_graph(
+                    lambda x: 1.0 / (1.0 + math.exp(-x)),
+                    use_smoothing=False,
+                    color=YELLOW,
+                    )
+        axes1 = Axes((-3, 5), (0, 5),
+                     y_axis_config={
+            "decimal_number_config": {
+                "num_decimal_places": 1,
+            }
+        },
+            width=10).scale(0.6).shift(0.5*DOWN)
+        relu_graph = axes1.get_graph(
+            lambda x: max(0, x),
+            use_smoothing=False,
+            color=YELLOW,
+        )
+        axes2 = Axes((-5, 5), (-1.2, 1.2),
+                     y_axis_config={
+                        "decimal_number_config": {
+                            "num_decimal_places": 1,
+                        }
+                    },
+                    width=10).scale(0.6).shift(0.5*DOWN)
+        tanh_graph = axes2.get_graph(
+            lambda x: (math.exp(x) - math.exp(-x)) / (math.exp(x) + math.exp(-x)),
+            use_smoothing=False,
+            color=YELLOW,
+        )
+
+        self.play(Write(axes, lag_ratio=0.01, run_time=1))
+        non_linear_label = axes.get_graph_label(sigmoid_graph, Text("Sigmoid(Logistic) Function").scale(0.4))
+        sigmoid_formula = axes.get_graph_label(
+            sigmoid_graph, Tex("\\frac{1}{1+e^{-z}}").scale(1.3),direction=RIGHT).shift(2*DOWN+0.5*RIGHT)
+        # sigmoid_formula = Tex("\\frac{1}{1+e^{-x}}").next_to(axes,DOWN).scale(1.3)
+        lbl = axes.y_axis.get_number_mobject(0.5,font_size=24)
+        tip = Line([-0.1,0.5,0],[0.1,0.5,0])
+        axes.add_coordinate_labels(x_values=[-5,5],y_values=[])
+        self.play(
+            ShowCreation(sigmoid_graph),
+            FadeIn(non_linear_label, RIGHT),
+            Write(sigmoid_formula),
+            Write(lbl),
+            Write(tip),
+        )
+        self.wait(6)
+        zero = axes.x_axis.get_number_mobject(0,font_size=24)
+        one = axes.y_axis.get_number_mobject(1,font_size=24)
+        self.play(Write(zero))
+        self.play(Write(one))
+        self.wait(4)
+        v_grp = VGroup(sigmoid_graph,non_linear_label,sigmoid_formula,lbl, tip,axes)
+        v_grp1 = v_grp.copy().shift(4*LEFT).scale(0.8)
+        self.wait()
+        NeuralNetwork.arguments['layer_sizes'] = [4,6,6,2]
+        NeuralNetwork.add_neurons(self,False)
+        NeuralNetwork.add_edges(self,False)
+        NeuralNetwork.group1(self)
+        self.grp.shift(3*RIGHT)
+        self.play(Transform(v_grp,v_grp1),FadeOut(VGroup(zero,one)))
+        self.play(Write(self.grp))
+        explain_binary_classification = TexText('(Classify a set of elements into 2 groups)').next_to(self.grp,DOWN).scale(0.8)
+        binary_classification = TexText('Binary Classification').next_to(self.grp,UP).scale(0.8)
+        cat_label = TexText('Cat').next_to(self.grp.layers[3][0][0]).scale(0.8)
+        dog_label = TexText('Dog').next_to(self.grp.layers[3][0][1]).scale(0.8)
+        cat = ImageMobject('assets/cat.png').next_to(self.grp.layers[0],LEFT).scale(0.4).shift(RIGHT*0.8)
+        self.play(Write(binary_classification))
+        self.play(Indicate(self.grp.layers[3]), Write(
+            VGroup(cat_label, dog_label)), FadeIn(cat))
+        self.wait()
+        self.play(Write(explain_binary_classification))
+        self.wait()
+        x = [[random.uniform(0, 1) for x1 in range(6)], [
+                random.uniform(0, 1) for x1 in range(6)]]
+        self.play(FadeTransform(cat,self.dot()))
+        self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill,
+                                WHITE, float(x[0][x1])) for x1 in range(6)])
+        self.wait()
+        self.play(*[ApplyMethod(self.grp.layers[2][0][x2].set_fill,
+                                WHITE, float(x[1][x2])) for x2 in range(6)])
+        self.wait()
+        self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill, WHITE, 1))
+        self.wait()
+        relu_label = axes1.get_graph_label(relu_graph, Text("ReLU Function").scale(0.4))
+        relu_formula = axes1.get_graph_label(
+            relu_graph, Tex("max(0,z)").scale(1.3),direction=RIGHT).shift(2.5*DOWN+RIGHT)
+        relu_title = TexText('Rectified Linear Unit').to_edge(UP).shift(DOWN)
+        relu_description = TexText('Simple, Effective, Less Computation').next_to(axes1,DOWN)
+        self.fade_all_except_title()
+        self.play(Write(axes1, lag_ratio=0.01, run_time=1))
+        self.play(
+            ShowCreation(relu_graph),
+            Write(relu_title),
+            # Write(lbl),
+            # Write(tip),
+        )
+        self.wait()
+        self.play(FadeIn(relu_label, RIGHT))
+        self.wait(2)
+        self.play(Write(relu_formula))
+        self.play(Indicate(relu_formula[0][0:3]))
+        self.play(Indicate(relu_formula[0][4],scale_factor=1.5))
+        self.play(Indicate(relu_formula[0][6]),scale_factor=1.5)
+        self.wait(2)
+        self.play(Indicate(relu_formula[0][6]), scale_factor=1.5)
+        self.wait(2.5)
+        self.play(Indicate(relu_formula[0][4], scale_factor=1.5))
+        self.wait(7)
+        self.play(Write(relu_description),run_time=3)
+        self.wait(2)
+        tanh_label = axes2.get_graph_label(
+            tanh_graph, Text("Tanh Function").scale(0.4))
+        tanh_formula = axes2.get_graph_label(
+            tanh_graph, Tex("\\frac{(e^z-e^{-z})}{(e^z+e^{-z})}").scale(0.8), direction=RIGHT).shift(1.5*DOWN+0.2*RIGHT)
+        tanh_title = TexText('Hyperbolic Tangent Function').to_edge(UP).shift(DOWN)
+        tanh_description = TexText(
+            'Used in Binary Classification\\\Performs better than Sigmoid Function').next_to(self.grp, DOWN).scale(0.8)
+        # self.embed()
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.shift(3*RIGHT)
+        relu = VGroup(axes1,relu_graph,relu_formula)
+        tanh = VGroup(axes2,tanh_graph,tanh_formula)
+        # self.embed()
+        self.play(ReplacementTransform(relu,tanh),FadeOut(VGroup(relu_description,relu_title,relu_label)))
+        self.play(Write(tanh_title))
+        self.wait()
+        self.play(Write(tanh_label))
+        self.wait(4)
+        one = axes2.y_axis.get_number_mobject(1,font_size=24)
+        negative_one = axes2.y_axis.get_number_mobject(-1,font_size=24)
+        self.play(Write(negative_one))
+        self.play(Write(one))
+        self.wait(2)
+        dot = Dot(color=BLUE)
+        dot.move_to(axes2.i2gp(0, tanh_graph))
+        self.play(GrowFromPoint(dot,dot.get_center(), scale=0.5))
+
+        x_tracker = ValueTracker(0)
+        f_always(
+            dot.move_to,
+            lambda: axes2.i2gp(x_tracker.get_value(), tanh_graph)
+        )
+
+        self.play(x_tracker.animate.set_value(4), run_time=3)
+        self.play(x_tracker.animate.set_value(-4), run_time=5)
+        self.wait(2)
+        # tanh_grp = VGroup(axes2, tanh_graph, tanh_label,
+        #                   tanh_formula, tanh_title)
+        tanh.add(tanh_label)
+        tanh_grp_copy = tanh.copy().shift(4*LEFT).scale(0.8)
+        self.play(Transform(tanh,tanh_grp_copy),FadeOut(VGroup(one,negative_one,dot)))
+        self.remove(x_tracker)
+        self.play(Write(self.grp))
+        self.play(Write(tanh_description))
+        self.wait()
+        self.play(Indicate(self.grp.layers[3]))
+
+        self.wait(5)
+        self.fade_all_except_title()
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 4]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.2).to_edge(RIGHT).shift(LEFT)
+        question = TexText('Which should you use?').move_to([3,3,0])
+        # self.add(question)
+        softmax = TexText('Softmax Function').to_edge(UP).shift(DOWN)
+        softmax_formula = Tex('\\text{Softmax}(x)=\\frac{e^z}{\sum e^z}')
+        self.play(Write(softmax))
+        self.play(Write(softmax_formula))
+        self.play(softmax_formula.animate.shift(3.5*LEFT))
+        self.play(Write(self.grp))
+
+        softmax_group = self.label('a',VGroup(),3)
+        self.play(Write(softmax_group))
+        softmax_sum = Tex('\sum_{a=0} = 1').next_to(self.grp.layers[2],UP)
+        softmax_group_copy = softmax_group.copy()
+        self.wait(5)
+        self.play(Transform(softmax_group_copy,softmax_sum))
+        softmax_application = TexText('Used in Multiclass Classification').next_to(self.grp,DOWN)
+        self.wait(2)
+        ans_layer = VGroup(Tex('0.1').scale(0.5).move_to(self.grp.layers[3][0][0]),Tex('0.5').scale(0.5).move_to(self.grp.layers[3][0][1]),Tex('0.2').scale(0.5).move_to(self.grp.layers[3][0][2]),Tex('0.1').scale(0.5).move_to(self.grp.layers[3][0][3])).set_color(BLUE)
+        self.play(*[Transform(softmax_group[i],ans_layer[i]) for i in range(len(ans_layer))])
+        self.wait(5)
+        self.play(Write(softmax_application))
+        self.wait(6)
+        self.fade_all_except_title()
+        self.play(Transform(self.title,TexText('How to choose Activation Function').to_edge(UP+LEFT)))
+        Choose.construct(self)
+        self.fade_all_except_title()
+        self.play(Transform(self.title,TexText('Bias: Further Explanation').to_edge(UP+LEFT)))
+        Bias.construct(self)
+        self.fade_all_except_title()
+        EndScreen.construct(self)
+        self.embed()
+
+    def first_part(self):
+        #asset, first part
+        self.title = TexText('What is Neural Network?').to_corner(UP+LEFT)
+        brain = SVGMobject('assets/brain.svg')
+        brain.flip(LEFT)
+        algorithm = TexText('Algorithm').next_to(brain,LEFT)
+        question = Tex('?').next_to(brain,RIGHT)
+        ans = TexText("It's an orange.").next_to(self.grp.layers[3][0][0],RIGHT)
+        question2nd = TexText('What is a neuron?').to_corner(UP+LEFT)
+        arrow = Arrow([-2,2.5,0],[2,2.5,0])
+        self.play(Write(self.title))
+        self.wait()
+        self.play(Write(VGroup(algorithm)))
+        self.play(DrawBorderThenFill(brain),Write(question))
+        self.wait()
+        self.play(VGroup(algorithm,question,brain).animate.to_edge(LEFT))
+        self.wait(2)
+        self.subset()
+        # self.embed()
+        # self.wait()
+        self.fade_all_except_title()
+
+        x = [[random.uniform(0, 1) for x1 in range(6)], [
+                random.uniform(0, 1) for x1 in range(6)]]
+
+        org = SVGMobject('assets/orange2.0.svg')
+        org.set_color(ORANGE).to_edge(LEFT).flip(LEFT)
+
+        self.hid_layer = Brace(VGroup(self.grp.layers[1],self.grp.layers[2]),UP)
+        self.in_layer_text = TexText('Input Layer').next_to(self.grp.layers[0],1.5*DOWN)
+        self.hid_layer_text = self.hid_layer.get_text('Hidden Layer').scale(0.8)
+        self.out_layer_text = TexText('Output Layer').next_to(self.grp.layers[3],1.5*DOWN)
+        t = Tex('248').move_to(self.grp.layers[0][0][0]).scale(0.5)
+        t1 = Tex('148').move_to(self.grp.layers[0][0][1]).scale(0.5)
+        t2 = Tex('6').move_to(self.grp.layers[0][0][2]).scale(0.5)
+        t3 = Tex('1').move_to(self.grp.layers[0][0][3]).scale(0.5)
+        self.texts = VGroup(t,t1,t2,t3)
+        self.add(self.grp)
+        org_transform = org.copy()
+        self.play(DrawBorderThenFill(org))
+        self.play(ReplacementTransform(org_transform,self.texts))
+        self.wait()
+        self.play(ShowCreation(arrow))
+        self.play(*[ApplyMethod(self.grp.layers[1][0][x1].set_fill,
+                                WHITE, float(x[0][x1])) for x1 in range(6)])
+        self.play(*[ApplyMethod(self.grp.layers[2][0][x2].set_fill,
+                                WHITE, float(x[1][x2])) for x2 in range(6)])
+        self.wait(4)
+        self.play(ApplyMethod(self.grp.layers[3][0][0].set_fill,WHITE,1))
+        self.wait()
+        self.play(Write(ans))
+        self.play(FadeOut(org),FadeOut(arrow),FadeOut(ans),FadeOut(self.texts))
+        self.wait(3)
+
+        #Layer
+        shade_area = Rectangle(height=FRAME_HEIGHT,width=FRAME_WIDTH-6).to_edge(RIGHT)
+        shade = BackgroundRectangle(shade_area)
+        shade_area_hid1 = Rectangle(height=FRAME_HEIGHT,width=5).to_edge(LEFT)
+        shade_area_hid2 = Rectangle(height=FRAME_HEIGHT,width=3.5).to_edge(RIGHT)
+        shade_area_out = Rectangle(height=FRAME_HEIGHT,width=FRAME_WIDTH-6).to_edge(LEFT)
+        shade_hid1 = BackgroundRectangle(shade_area_hid1)
+        shade_hid2 = BackgroundRectangle(shade_area_hid2)
+        shade_out = BackgroundRectangle(shade_area_out)
+        org_transform = org.copy()
+
+        self.play(Write(self.in_layer_text),ShowCreation(shade))
+        self.wait(4)
+        self.play(DrawBorderThenFill(org))
+        self.bring_to_back(org)
+        self.play(ReplacementTransform(org_transform,self.texts))
+        self.play(FadeOut(self.texts))
+        self.wait(3)
+        self.play(ShowCreationThenDestruction(self.edges[0]))
+        self.wait(2)
+        self.play( GrowFromCenter(self.hid_layer), Write(self.hid_layer_text),ReplacementTransform(shade.copy(),shade_hid2),ReplacementTransform(shade,shade_hid1))
+        self.bring_to_back(self.hid_layer)
+        self.bring_to_back(self.hid_layer_text)
+        self.wait(2)
+        self.play(ShowCreationThenDestruction(self.edges[1]))
+        self.wait(3)
+        self.play(ShowCreationThenDestruction(self.edges[2]))
+        self.wait(2)
+        shade_out_copy = shade_out.copy()
+        self.play(Write(
+            self.out_layer_text), ReplacementTransform(shade_hid2, shade_out),ReplacementTransform(shade_hid1, shade_out_copy))
+        self.play(Write(ans),FadeOut(shade_out_copy))
+        self.wait(2)
+        grp=self.grp
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6,6,6, 1]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(0.7).to_corner(UP+RIGHT)
+
+        # # #explanation: speech recongition example
+        self.remove(self.in_layer_text,org,self.hid_layer_text,self.hid_layer)
+        self.play(FadeOut(VGroup(self.out_layer_text, shade_out, ans)))
+        grp_fade = VGroup()
+        print('---')
+        for mob in self.mobjects:
+            grp_fade.add(mob) if mob is not self.title else None
+            print(mob)
+        # self.embed()
+        # self.fade_all_except_grp(grp)
+        # self.add(grp)
+        self.wait()
+        self.play(ReplacementTransform(grp, self.grp))
+
+        explanation = VGroup()
+        soundwave = SVGMobject('assets/soundwavee.svg').scale(0.5).to_edge(LEFT)
+        arrow = Arrow([0,0,0],[1,0,0]).next_to(soundwave,RIGHT)
+        explanation1 = TexText('Frequency/Pitch of Sound').next_to(arrow,RIGHT)
+        arrow1 = Arrow([0, 0, 0], [1, 0, 0]).next_to(explanation1, RIGHT)
+        explanation2 = TexText('Phonemes').next_to(arrow1,RIGHT)
+        explanation20 = TexText('Code').next_to(explanation2,UP)
+        explanation21 = TexText('C o d e').next_to(explanation2,UP)
+        arrow2 = Arrow([0,0,0],[0,-1,0]).next_to(explanation2,DOWN)
+        explanation3 = TexText('Word').next_to(arrow2,DOWN)
+        arrow3 = Arrow([0, 0, 0], [-1, 0, 0]).next_to(explanation3, LEFT)
+        explanation4 = TexText('Phrases').next_to(arrow3,LEFT)
+        arrow4 = Arrow([0, 0, 0], [-1, 0, 0]).next_to(explanation4, LEFT)
+        output = TexText('Hello World').next_to(arrow4,LEFT)
+        explanation.add(arrow,explanation1,arrow1,explanation2,arrow2,explanation3,arrow3,explanation4,arrow4)
+        self.wait(3)
+        self.play(DrawBorderThenFill(soundwave),Indicate(self.grp.layers[0],color=YELLOW, run_time=2))
+        self.wait(3)
+        self.play(Write(VGroup(arrow,explanation1)),Indicate(self.grp.layers[1],color=YELLOW, run_time=2))
+        self.wait(3)
+        self.play(Write(VGroup(arrow1,explanation2)),Indicate(self.grp.layers[2],color=YELLOW, run_time=2))
+        self.wait(9)
+        self.play(Write(explanation20))
+        self.wait(3)
+        self.play(TransformMatchingTex(explanation20,explanation21,path_arc = PI/2))
+        self.wait(4)
+        self.play(Write(VGroup(arrow2, explanation3)), Indicate(
+            self.grp.layers[3], color=YELLOW, run_time=2))
+        self.wait(2)
+        self.play(Write(VGroup(arrow3, explanation4)),Indicate(self.grp.layers[4],color=YELLOW, run_time=2))
+        self.wait(3.5)
+        self.play(Write(VGroup(arrow4,output)),Indicate(self.grp.layers[5],color=YELLOW, run_time=2))
+        self.wait(8)
+        hp = TexText('Based on Human Perspective').to_edge(LEFT).shift(2*UP).to_edge(LEFT)
+        not_exactly = TexText("Not Exactly What's Happening").next_to(hp,DOWN).to_edge(LEFT)
+        self.play(Write(hp))
+        self.wait(9)
+        self.play(Write(not_exactly))
+        self.wait(3)
+        self.play(FadeOut(VGroup(explanation,output,explanation21)))
+        self.play(ReplacementTransform(soundwave.copy(),explanation),run_time=2)
+        self.wait(2)
+        self.play(Indicate(self.grp.layers[1]),run_time=0.5)
+        self.play(Indicate(self.grp.layers[2]),run_time=0.5)
+        self.play(Indicate(self.grp.layers[3]),run_time=0.5)
+        self.play(Indicate(self.grp.layers[4]), run_time=0.5)
+        self.play(ReplacementTransform(explanation.copy(),output))
+        self.play(Indicate(self.grp.layers[5], color=YELLOW, run_time=2))
+        self.wait(1.5)
+
+        grp = self.grp
+        NeuralNetwork.arguments['layer_sizes'] = [4, 6, 6, 2]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        self.grp.scale(1.5)
+        self.fade_all_except_grp(grp)
+        self.play(ReplacementTransform(grp, self.grp))
+
+        # self.embed()
+        self.wait()
+        self.play(Transform(self.title, question2nd))
+        self.wait(12)
+        self.decimal = DecimalNumber(
+            0, num_decimal_places=3, include_sign=False, unit=None)
+        self.decimal.add_updater(lambda d: d.set_value(
+            float(self.grp.layers[1][0][3].get_fill_opacities()[0])))
+        self.decimal.scale(0.2).move_to(
+            self.grp.layers[1][0][3].set_fill(WHITE, 0))
+        self.play(self.camera.frame.scale, 0.1,
+                  self.camera.frame.move_to, self.grp.layers[1][0][3])
+        self.play(FadeOut(self.grp.edges))
+        self.bring_to_front(self.decimal)
+        self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 0))
+        self.wait()
+        self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 1))
+        self.wait()
+        self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 0))
+        self.wait()
+        self.play(ApplyMethod(self.grp.layers[1][0][3].set_opacity, 1))
+        self.wait()
+
+
+
+
+    def shallow_nn_representation(self):
+        NeuralNetwork.arguments['layer_sizes'] = [3,1,1]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False)
+        NeuralNetwork.group1(self)
+        l = "x"
+
+        self.grp.scale(3)
+        self.grp.layers[1][0][0].scale(2)
+        input_group = VGroup()
+
+        for x in range(NeuralNetwork.arguments['layer_sizes'][0]):
+            label = Tex(f"{l}_"+"{"+f"{x+1}"+"}")
+            label.move_to(self.grp.layers[0][0][x])
+            input_group.add(label)
+        y_hat = Tex('\hat{y}')
+        y_hat.move_to(self.grp.layers[2][0][0])
+        sigma = Tex('\sigma').shift(0.5*DOWN)
+        line = Line(self.grp.edges[0][1],self.grp.edges[1][0])
+        log_reg_formula = Tex('w^Tx+b').scale(0.8).shift(0.4*UP)
+        self.play(Write(self.grp),Write(input_group), Write(
+            log_reg_formula), Write(line), Write(sigma),Write(y_hat))
+        return
+
+    def weights(self):
+        NeuralNetwork.arguments['layer_sizes'] = [3, 1, 1]
+        NeuralNetwork.add_neurons(self, False)
+        NeuralNetwork.add_edges(self, False, False)
+        NeuralNetwork.group1(self)
+        l = "x"
+        weight = TexText('What is weight?').to_corner(UP+LEFT)
+        self.grp.scale(3)
+        self.grp.layers[1][0][0].scale(2)
+        input_group = VGroup()
+        input_label = TexText('Input').next_to(self.grp.layers[0][0][0], UP)
+        y_hat_label = TexText('Predicted Output').next_to(
+            self.grp.layers[2][0][0], UP)
+        for x in range(NeuralNetwork.arguments['layer_sizes'][0]):
+            label = Tex(f"{l}_"+"{"+f"{x+1}"+"}")
+            label.move_to(self.grp.layers[0][0][x])
+            input_group.add(label)
+        y_hat = Tex('\hat{y}')
+        y_hat.move_to(self.grp.layers[2][0][0])
+        shallow_nn = VGroup(self.grp, input_group, y_hat)
+        self.play(Write(shallow_nn),
+                    Write(input_label), Write(y_hat_label),
+                    Transform(self.title,weight))
+        self.bring_to_back(self.grp.layers[0])
+        self.play(ZoomInThenZoomOut(self.grp.layers[0], color=WHITE),ZoomInThenZoomOut(input_group))
+        self.wait()
+        self.play(ZoomInThenZoomOut(self.grp.layers[1],color=WHITE))
+        dashed_line_group = VGroup(*[DashedLine(self.grp.edges[0][x].get_center(), [0, 2.5, 0]) for x in range(
+            NeuralNetwork.arguments['layer_sizes'][0])], VGroup(DashedLine(self.grp.edges[1][0].get_center(), [0, 2.5, 0])))
+        w_layer_group = VGroup()
+        l = "w"
+        synapse = TexText('Edges (Connections)').next_to(
+            dashed_line_group[0].get_end(), UP)
+        for x in range(NeuralNetwork.arguments['layer_sizes'][0]):
+            label = Tex(f"{l}_"+"{"+f"{x+1}"+"}")
+            label.move_to(self.grp.edges[0][x]).shift(0.4*UP)
+            w_layer_group.add(label)
+        self.wait()
+        self.play(Write(dashed_line_group))
+        self.play(Write(synapse))
+        self.wait()
+        self.play(FadeOut(synapse), FadeOut(dashed_line_group))
+        self.wait()
+        self.play(Write(w_layer_group))
+        self.wait(1.5)
+        self.play(ZoomInThenZoomOut(w_layer_group[0]))
+        self.wait(4)
+        self.play(ZoomInThenZoomOut(self.grp.edges[0]))
+        self.wait(7)
+        arrow = Arrow([0, 0, 0], [0, 1, 0]).next_to(w_layer_group[0], RIGHT)
+        arrow3 = Arrow([0, 1, 0], [0, 0, 0]).next_to(w_layer_group[0], RIGHT)
+
+        # zero = Tex('0').next_to(w_layer_group[0], RIGHT)
+        v = ValueTracker(0)
+        x1 = DecimalNumber(0, num_decimal_places=1, include_sign=False, unit=None).move_to(
+            self.layers[0][0][0].get_center()).scale(0.5)
+        z = DecimalNumber(0, num_decimal_places=2,
+                          include_sign=False, unit=None)
+        x1.add_updater(lambda m: m.set_value(v.get_value()))
+        z.add_updater(lambda d: d.set_value(
+            float(self.grp.layers[1][0][0].get_fill_opacities()[0])))
+
+        self.grp.layers[1][0][0].set_fill(WHITE, 0)
+        self.play(ZoomInThenZoomOut(self.grp.layers[0][0][0]))
+        self.bring_to_back(self.grp.layers[0])
+        self.wait()
+        self.play(ZoomInThenZoomOut(self.grp.layers[1][0][0],color=WHITE))
+        self.wait(2)
+        self.play(Write(arrow), ReplacementTransform(input_group[0], x1))
+        self.add(z)
+
+        self.play(v.animate.set_value(7),
+                  ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.8), run_time=2)
+        self.wait(2)
+        self.grp.layers[1][0][0].set_fill(WHITE, 0)
+        v.set_value(0)
+        self.play(Transform(arrow, arrow3))
+        self.play(v.animate.set_value(7),
+                  ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.4), run_time=2)
+        self.wait(3)
+        self.remove(x1, z,v)
+        self.play(FadeOut(VGroup(self.grp, arrow, w_layer_group, input_group,input_label,y_hat_label,y_hat)))
+        # self.play(ReplacementTransform(arrow,zero),FadeOut(arrow1))
+        # self.grp.layers[1][0][0].set_fill(WHITE,0)
+        # self.play(ShowCreationThenFadeOut(arrow2),run_time=0.5)
+        # arrow2.flip(LEFT)
+        # self.play(ShowCreationThenFadeOut(arrow2),run_time=0.5)
+        # self.wait()
+
+        # self.play(ReplacementTransform(zero,arrow3))
+        # self.play(ApplyMethod(self.grp.layers[1][0][0].set_fill, WHITE, 0.2))
+
+    def nn_representation(self):
+        return
+
+    def label(self,l,group,layer=0,edges=False,bias=False):
+        if bias is True:
+            for x in range(len(NeuralNetwork.arguments['layer_sizes'])-1):
+                label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8)
+                label.move_to(self.bias_layer[x])
+                group.add(label)
+        else:
+            for x in range(NeuralNetwork.arguments['layer_sizes'][layer]):
+                label = Tex(f"{l}_"+"{"+f"{x}"+"}").scale(0.8)
+
+                label.move_to(self.grp.edges[layer][x]).shift(
+                    0.4*UP) if edges is True else label.move_to(self.grp.layers[layer][0][x])
+                group.add(label)
+
+        return group
+
+    def connections(self,x,y,z,wait=1,highlight=False):
+        i=0
+        while i != NeuralNetwork.arguments['layer_sizes'][0]:
+            self.edge_transform_1.add(self.edges[0][x])
+            print(0,x)
+            i+=1
+            x+=NeuralNetwork.arguments['layer_sizes'][1]
+
+
+        self.play(ShowCreationThenDestruction(self.edge_transform_1))
+        self.wait(wait)
+        if highlight is True:
+            self.play(Indicate(self.grp.layers[1][0][0],color=WHITE))
+            self.wait(2)
+        self.play(*[ShowCreationThenDestruction(self.edges[1][i]) for i in range(y,z)])
+
+    def dot(self):
+        dot_layer = VGroup(*[Dot().move_to(self.grp.layers[0][0][i].get_center()) for i in range(NeuralNetwork.arguments['layer_sizes'][0])])
+        dot_layer.set_fill(WHITE,0)
+        return dot_layer
+
+    def fade_all_except_title(self):
+        grp_fade = VGroup()
+        print('---')
+        for mob in self.mobjects:
+            grp_fade.add(mob) if mob is not self.title else None
+            print(mob)
+        self.play(
+            FadeOut(grp_fade)
+        )
+
+    def subset(self):
+        nn = TexText('Neural Network')
+        dl = TexText('Deep Learning').shift(1.3*UP)
+        ml = TexText('Machine Learning').shift(2.3*UP)
+        c = Circle(stroke_color=RED).stretch(2, dim=0)
+        c1 = Circle(radius=1.5, stroke_color=GREEN).stretch(
+            2, dim=0).shift(0.5*UP)
+        c2 = Circle(radius=2, stroke_color=BLUE).stretch(2, dim=0).shift(UP)
+        subset = VGroup(nn,dl,ml,c,c1,c2).scale(0.8).to_edge(RIGHT).shift(DOWN)
+        self.play(Write(VGroup(c, nn)))
+        self.wait()
+        self.play(Write(VGroup(c1, dl)))
+        self.wait()
+        self.play(Write(VGroup(c2, ml)))
+
+    def fade_all_except_grp(self,grp):
+        grp_fade = VGroup()
+        for mob in self.mobjects:
+            grp_fade.add(mob) if mob is not grp else None
+        self.play(
+            FadeOut(grp_fade)
+        )
+
+# color detection
+# edge detection
+# group detection
+#TODO
diff --git a/graph_scene.py b/graph_scene.py
new file mode 100644
index 0000000..84672fc
--- /dev/null
+++ b/graph_scene.py
@@ -0,0 +1,60 @@
+from manimlib import *
+
+class graph_scene(Scene):
+    def construct(self):
+
+        axes = Axes((-5, 5), (0, 1),
+                    y_axis_config={
+            "decimal_number_config": {
+                "num_decimal_places": 1,
+            }
+        },
+            width=10).scale(0.4).shift(0.5*UP)
+        sigmoid_graph = axes.get_graph(
+            lambda x: 1.0 / (1.0 + math.exp(-x)),
+            use_smoothing=False,
+            color=YELLOW,
+        )
+        axes1 = Axes((-3, 5), (0, 5),
+                    y_axis_config={
+            "decimal_number_config": {
+                "num_decimal_places": 1,
+            }
+        },
+            width=10).scale(0.4).shift(0.5*DOWN)
+        relu_graph = axes1.get_graph(
+            lambda x: max(0, x),
+            use_smoothing=False,
+            color=YELLOW,
+        )
+        axes2 = Axes((-5, 5), (-1, 1),
+                    y_axis_config={
+            "decimal_number_config": {
+                "num_decimal_places": 1,
+            }
+        },
+            width=10).scale(0.4).shift(0.5*DOWN)
+        tanh_graph = axes2.get_graph(
+            lambda x: (math.exp(x) - math.exp(-x)) /
+            (math.exp(x) + math.exp(-x)),
+            use_smoothing=False,
+            color=YELLOW,
+        )
+
+        sigmoid_name = TexText('Sigmoid Function').next_to(axes,DOWN)
+        relu_name = TexText('ReLU Function').next_to(axes1,DOWN)
+        tanh_name = TexText('Tanh Function').next_to(axes2,DOWN)
+        softmax_formula = Tex('\\text{Softmax}(x)=\\frac{e^z}{\sum e^z}')
+        softmax_name = TexText('Softmax Function').next_to(softmax_formula,DOWN)
+
+        sigmoid_grp = VGroup(axes,sigmoid_graph,sigmoid_name).shift(1.2*UP+3*LEFT)
+        relu_grp = VGroup(axes1,relu_graph,relu_name).shift(2.2*UP+3*RIGHT)
+        tanh_grp = VGroup(axes2,tanh_graph,tanh_name).shift(1.5*DOWN+3*LEFT)
+        softmax_grp = VGroup(softmax_name,softmax_formula).shift(2*DOWN+3*RIGHT)
+        self.play(Write(sigmoid_grp))
+        self.wait()
+        self.play(Write(relu_grp))
+        self.wait()
+        self.play(Write(tanh_grp))
+        self.wait()
+        self.play(Write(softmax_grp))
diff --git a/nn.py b/nn.py
new file mode 100644
index 0000000..916c2ac
--- /dev/null
+++ b/nn.py
@@ -0,0 +1,407 @@
+from manimlib import *
+import random
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+red1 = "#FF0000"
+blue1 = "#0000ff"
+light_grey1 = "#6b6b78"
+green1 = "#00FF00"
+violet1 = "#FF00FF"
+
+
+class NeuralNetwork(Scene):
+    arguments = {
+        "network_size": 1,
+        "network_position": ORIGIN,
+        "layer_sizes": [6, 12, 10, 8, 6, 2],
+        "layer_buff": 1.25*LARGE_BUFF,
+        # "layer_buff": LARGE_BUFF,
+        "neuron_radius": 0.15,
+        "neuron_color": WHITE,
+        "neuron_width": 2,
+        "neuron_fill_color": BLACK,
+        "neuron_fill_opacity": 1,
+        "neuron_buff": MED_SMALL_BUFF,
+        "edge_color": YELLOW_B,
+        "edge_width": 2,
+        "edge_opacity": 1,
+        "layer_label_color": WHITE,
+        "layer_label_size": 0.5,
+        "neuron_label_color": WHITE,
+    }
+
+    # def __init__(self):
+    #     self.nPclass = 0
+    #     self.nSex = 0
+    #     self.nAge = 0
+    #     self.nSibSp = 0
+    #     self.Fare = 0
+    #     self.Embarked = 0
+    #     self.Age_was_missing = 0
+    # def setup(self):
+    #     MovingCameraScene.setup(self)
+
+    def construct(self):
+        # self.camera_frame.save_state()
+
+        self.add_neurons()
+        self.add_edges()
+        # self.label_stuff()
+        self.group1()
+        # self.preload()
+        # self.grp.n = self.n
+        # self.grp.output_label = self.output_label
+        # self.grp.add(self.n)
+        # self.grp.add(self.output_label)
+        self.intro()
+        self.grp.n = self.n
+        self.grp.add(self.n)
+
+        # self.content()
+        # grp = VGroup()
+        # grp.layers = self.layers
+        # grp.edges = self.edge_groups
+        # grp.n = self.n
+        # grp.output_label = self.output_label
+        # grp.add(self.layers)
+        # grp.add(self.edge_groups)
+        # grp.add(self.n)
+        # grp.add(self.output_label)
+        # grp.shift(LEFT)
+        # self.play(ApplyMethod(grp.scale,0.72))
+        # self.label_layers()
+        # box = Rectangle(height=30, color=YELLOW, stroke_width=4)
+        # box.surround(self.n)
+        # self.play(ShowCreation(box))
+        # grp1 = VGroup()
+        # grp1.add(self.layers[1][0])
+        # grp1.add(self.layers[2][0])
+        # grp1.add(self.layers[3][0])
+        # grp1.add(self.layers[4][0])
+        # grp1.add(self.output_label)
+        # bland = Rectangle(height=20,fill_color=BLACK, fill_opacity=0.4, stroke_opacity=0)
+        # bland.surround(grp1)
+        # self.play(ShowCreation(bland))
+        return self.grp
+
+    def group1(self):
+        grp = VGroup()
+        grp.layers = self.layers
+        grp.edges = self.edge_groups
+        grp.add(self.layers)
+        grp.add(self.edge_groups)
+        self.grp = grp
+        return grp
+
+    def intro(self):
+        grp1 = self.grp.copy().scale(0.8).shift(3*RIGHT)
+        # self.play(FadeIn(self.grp))
+        # self.play(ShowCreation(grp1))
+        self.play(Transform(self.grp, grp1))
+        neural = TexText("Neural")
+        title = VGroup(
+            neural,
+            TexText("Network Crash"),
+            TexText("Course")
+        ).arrange(DOWN, aligned_edge=LEFT).shift(3.5*LEFT).scale(1.1)
+        line1 = Line(title.get_corner(UP+LEFT), neural.get_corner(UP+RIGHT),
+                     color=YELLOW_E, stroke_width=5).next_to(title, UP).shift(LEFT)
+        line = Line(title.get_corner(DOWN+LEFT), title.get_corner(DOWN+RIGHT),
+                    color=YELLOW_E, stroke_width=5).next_to(title, DOWN)
+        self.play(Write(title), ShowCreation(line), ShowCreation(line1))
+        ml = Text('Machine Learning:', font="consolas", font_size=24)
+        self.quote = VGroup(
+            ml,
+            Text("Programming with examples,", font="consolas",
+                 font_size=24, t2c={"examples": YELLOW}),
+            Text("not instructions.", font="consolas",
+                 font_size=24, t2c={"instructions": YELLOW}),
+            Tex('-\\ Kyle\\ Mcdonald')
+        ).arrange(DOWN, aligned_edge=LEFT).to_edge(LEFT).shift(0.5*RIGHT)
+
+        # mob1.align_to(mob2, alignment_vect = RIGHT)
+        self.introgrp = VGroup(title, line, line1)
+        title.add(line1)
+        title.add(line)
+        self.title = title
+        self.animation(self.grp)
+
+    def add_neurons(self, animation=True):
+        self.loop = False
+        layers = VGroup(*[self.get_layer(size)
+                        for size in NeuralNetwork.arguments["layer_sizes"]])
+        layers.arrange(RIGHT, buff=NeuralNetwork.arguments["layer_buff"])
+        layers.scale(NeuralNetwork.arguments["network_size"])
+        # self.layers is layers, but we can use it throughout every method in our class
+        # without having to redefine layers each time
+        self.layers = layers
+        layers.shift(NeuralNetwork.arguments["network_position"])
+        self.play(FadeInFromPoint(layers, ORIGIN),
+                  run_time=2) if animation is True else None
+        # self.add(layers)
+
+    def get_layer(self, size):
+        layer = VGroup()
+        n_neurons = size
+        neurons = VGroup(*[
+            Circle(
+                radius=NeuralNetwork.arguments["neuron_radius"],
+                stroke_color=NeuralNetwork.arguments["neuron_color"],
+                stroke_width=NeuralNetwork.arguments["neuron_width"],
+                fill_color=NeuralNetwork.arguments["neuron_fill_color"],
+                fill_opacity=NeuralNetwork.arguments["neuron_fill_opacity"],
+            )
+            for i in range(n_neurons)
+        ])
+        neurons.arrange(DOWN, buff=NeuralNetwork.arguments["neuron_buff"])
+        layer.neurons = neurons
+        layer.add(neurons)
+        if size == 6 and self.loop == False:
+            self.neurons1 = neurons
+            self.loop = True
+        return layer
+
+    def edge_security(self):
+        self.edge_groups = VGroup()
+        for l1, l2 in zip(self.layers[:-1], self.layers[1:]):
+            edge_group = VGroup()
+            for n1, n2 in it.product(l1.neurons, l2.neurons):
+                edge = self.get_edge(n1, n2)
+                edge_group.add(edge)
+            self.edge_groups.add(edge_group)
+
+    def add_edges(self, animation=True, color=True):
+        self.edge_groups = VGroup()
+        for l1, l2 in zip(self.layers[:-1], self.layers[1:]):
+            edge_group = VGroup()
+            for n1, n2 in it.product(l1.neurons, l2.neurons):
+                # print(x,y)
+                edge = self.get_edge(n1, n2, color)
+                edge_group.add(edge)
+
+            # edge_group = self.set_bias(x,edge_group) if x is not len(NeuralNetwork.arguments['layer_sizes'])-2 else None
+            self.play(Write(edge_group),
+                      run_time=0.5) if animation is True else None
+            self.edge_groups.add(edge_group)
+
+    def get_edge(self, neuron1, neuron2, color):
+        colors = [red1, blue1, blue1]
+        r = random.randint(0, len(colors)-1)
+        # if y % self.arguments['layer_sizes'][x+1] == 0 and x is not len(self.arguments['layer_sizes'])-2:
+        #     print(x,y)
+        #     color = GREY
+        # else:
+        #     color = colors[r]
+        # print(y%NeuralNetwork.arguments['layer_sizes'][x+1] != 0,x,y)
+        return Line(
+            neuron1.get_center(),
+            neuron2.get_center(),
+            # buff=1.25*NeuralNetwork.arguments["neuron_radius"],
+            buff=NeuralNetwork.arguments["neuron_radius"],
+
+            stroke_color=GREY if color is False else colors[r],
+            stroke_width=NeuralNetwork.arguments["edge_width"],
+            stroke_opacity=NeuralNetwork.arguments["edge_opacity"]
+        )
+
+    def set_bias(self, x):
+        x1 = 0
+        # return None
+        for i in range(self.arguments['layer_sizes'][x]):
+            print(x, x1)
+            self.edge_groups[x][x1].set_color(GREY)
+            x1 += self.arguments['layer_sizes'][x+1]
+        # self.edge_groups[0][0].set_color(GREY)
+
+    def animation(self, grop, backward_teaser=False, L=1):
+        input_data1 = self.read_data('test.csv', False)
+        # print(input_data)
+        output = self.read_data('my_submission.csv', False)
+
+        # output_data = self.read_data('ans.csv',False)
+        # for i in range(len(input_data1)):
+        # y=None
+        if backward_teaser is True:
+            y = np.array(pd.read_csv(
+                'data/without_training/output_real.csv').T.reset_index().drop('index', axis=1)[0])
+            y_hat = np.array(pd.read_csv(
+                'data/without_training/output.csv').T.reset_index().drop('index', axis=1)[0])
+            p = np.zeros((1, y_hat.shape[0]))
+            for i in range(0, y_hat.shape[0]):
+                if y_hat[i] > 0.5:
+                    p[0, i] = 1
+                else:
+                    p[0, i] = 0
+            print(y[0])
+            self.ans = Tex(str(y[1])).next_to(
+                self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT)
+
+        self.activate = False
+        ans_output = False
+        # always(self.ans.next_to,
+        #     self.grp.layers[len(self.arguments['layer_sizes'])-1], 2*RIGHT)
+        for i in range(L):
+            self.input_data(input_data1.T[i], True, backward_teaser=True, y=y, i=i+1) if backward_teaser is True else self.input_data(
+                input_data1.T[i], True)
+
+            # self.wait()
+            for i1 in range(4):
+                df = self.read_data('activation'+str(i1+1)+'.csv',
+                                    True) if backward_teaser is False else self.read_data('without_training/activation'+str(i1+1)+'.csv', True)
+                # for x in range(len(df.T)):
+                #     print(df[x][i])
+                self.play(*[ApplyMethod(grop.layers[i1+1][0][x].set_fill,
+                                        WHITE, float(df[x][i])) for x in range(len(df.T))])
+                if i1 == 1 and backward_teaser is False:
+                    print("DEY")
+                    self.play(TransformMatchingParts(
+                        self.title, self.quote, transform_mismatches=True))
+
+            print(output['Survived'][i], 6)
+            self.output_data(output['Survived'][i], len(self.arguments['layer_sizes'])-1) if backward_teaser is False else self.output_data(
+                int(list(p[0])[i+1]), len(self.arguments['layer_sizes'])-1, y[i+1])
+            if backward_teaser is True:
+                # self.wait()
+                self.grp.layers.set_fill(WHITE, 0)
+                self.play(FadeOut(self.fade_out))
+            if backward_teaser is True and i == 1:
+                accuracy = TexText('Accuracy: 38\%').move_to([4, 3, 0])
+                self.play(Write(accuracy)) if ans_output is False else None
+                ans_output = True
+                self.wait()
+                weights_random = TexText('Weights are initialized randomly!').next_to(
+                    self.grp.layers[2], UP).to_edge(LEFT).shift(RIGHT+0.5*DOWN)
+                grp = self.grp.copy()
+                grp.shift(DOWN).scale(0.8)
+                n = self.n.copy()
+                for i in range(6):
+                    n[i].move_to(self.grp.layers[0][0][i].get_center())
+                self.play(self.grp.animate.shift(DOWN).scale(0.8), accuracy.animate.shift(2.5*DOWN), self.ans.animate.next_to(
+                    grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT), Transform(self.n, n))
+                self.play(Write(weights_random))
+                self.play(
+                    Write(TexText('= Guessing Game!').next_to(weights_random, RIGHT)))
+        #self.play( self.camera_frame.scale,0.05,self.camera_frame.move_to,self.layers[0][0][0])
+        #self.wait()
+
+    def preload(self):
+        input_data1 = self.read_data('test.csv', False)
+        self.input_data(input_data1.T[0], False)
+        print(1)
+
+    def label_layers(self):
+
+        braces = Brace(self.layers[0], LEFT)
+        self.t = braces.get_text("Input Layer").scale(0.8).shift(0.5*RIGHT)
+        # t = TexText("Input Layer", tex_to_color_map={'Input': WHITE}).next_to(braces,LEFT).scale(0.8)
+        self.play(GrowFromCenter(braces), Write(self.t))
+        self.wait()
+        grp = VGroup()
+        # for i in range(4):
+        #     grp.add(self.layers[i+1][0][0].get_center())
+        line = Line(self.grp.layers[1][0][0].get_center(
+        ), self.grp.layers[4][0][0].get_center())
+
+        braces1 = Brace(line, direction=line.copy().rotate(
+            PI / 2).get_unit_vector())
+        t1 = braces1.get_text("Hidden Layer").scale(0.8).shift(0.5*RIGHT)
+
+        self.play(GrowFromCenter(braces1), Write(t1))
+        braces2 = Brace(self.grp.output_label, RIGHT)
+        t2 = braces2.get_text("Output Layer").scale(0.8).shift(0.5*LEFT)
+        self.play(GrowFromCenter(braces2), Write(t2))
+
+    def read_data(self, fil, activation):
+        foo = pd.read_csv('data/'+fil)
+        if activation == True:
+            scaler = MinMaxScaler()
+            d = scaler.fit_transform(foo.T)
+            scaled_df = pd.DataFrame(d)
+            scaled_df, scaled_df_selected = train_test_split(
+                scaled_df, test_size=0.1, random_state=42)
+
+        else:
+
+            scaled_df, scaled_df_selected = train_test_split(
+                foo, test_size=0.1, random_state=42)
+
+        return scaled_df_selected.reset_index().drop('index', axis=1)
+
+    def input_data(self, a, animate, backward_teaser=False, y=None, i=None):
+
+        n = VGroup()
+        for s in a:
+            t = Tex(str(round(int(s), 2)))
+            n.add(t)
+        n.arrange(DOWN, buff=0.3)
+        n.next_to(self.grp.layers, LEFT)
+        ans = Tex(str(y[i])).next_to(
+            self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT).shift(RIGHT) if backward_teaser is True else None
+        print(ans, 'ans') if backward_teaser is True else None
+        n1 = n.copy()
+        n1.scale(0.2)
+        for i in range(6):
+            n1[i].move_to(self.neurons1[i].get_center())
+        if animate is True:
+            try:
+                self.play(FadeOut(self.n))
+            except:
+                pass
+            print('hello')
+            if backward_teaser is True:
+                self.play(Transform(n, n1), Write(self.ans)) if self.activate is False else self.play(
+                    Transform(n, n1), Transform(self.ans, ans))
+                self.activate = True
+            else:
+                self.play(Transform(n, n1))
+            self.n = n
+        else:
+            self.n = n1
+
+    def label_stuff(self):
+        a = [str(random.randint(1, 10)) for i in range(2)]
+        n = VGroup()
+        survived = Tex('Survive').scale(0.8)
+        not_survived = Tex('Not Survive').scale(0.8)
+        n.add(survived)
+        n.add(not_survived)
+        n.arrange(DOWN, buff=MED_SMALL_BUFF)
+        n.next_to(self.layers, RIGHT)
+        self.play(Write(n))
+        title = TexText("Neural Network of Titanic Dataset",
+                        tex_to_color_map={'Neural': WHITE}).to_edge(UP)
+        self.play(Write(title))
+        self.output_label = n
+
+    def output_data(self, a, layer, y=None):
+        print(a, layer, 'output')
+        if self.arguments['layer_sizes'][-1] != 1 and y is None:
+            self.layers[layer][0][0].set_fill(WHITE, 0)
+            self.layers[layer][0][1].set_fill(WHITE, 0)
+            self.play(ApplyMethod(self.layers[layer][0][a].set_fill, WHITE, 1))
+        elif self.arguments['layer_sizes'][-1] is 1 and y is not None:
+            not_equal = Tex('!=', color=RED).next_to(
+                self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT)
+            equal = Tex('==', color=GREEN).next_to(
+                self.grp.layers[len(self.arguments['layer_sizes'])-1], RIGHT)
+            self.play(ApplyMethod(self.layers[layer][0][0].set_fill, WHITE, a))
+            self.play(Write(not_equal)) if a != y else self.play(
+                Write(equal))
+            self.wait()
+            self.fade_out = not_equal if a != y else equal
+
+    def content(self):
+        content = VGroup(
+            TexText("Course Overview:"),
+            TexText("1. What is Neural Network?"),
+            TexText("2. Neuron"),
+            TexText('3. Weights,'),
+            TexText('Bias and Connections').shift(RIGHT),
+            TexText("4. Layers")
+        ).arrange(DOWN, aligned_edge=LEFT).to_edge(UP+LEFT)
+        self.grp.add(self.t)
+
+        self.play(ReplacementTransform(self.t, content))
+        self.grp.remove(self.t)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..f96ebfe
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+manimgl
+scikit-learn
+numpy
+pandas