diff --git a/.gitignore b/.gitignore index 8579342..3bdc019 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,5 @@ /node_modules # 11ty -/_site \ No newline at end of file +/_site +/public \ No newline at end of file diff --git a/_assets/img/2020-07-28_23-39.png b/_assets/img/2020-07-28_23-39.png deleted file mode 100644 index 30f898d..0000000 Binary files a/_assets/img/2020-07-28_23-39.png and /dev/null differ diff --git a/admin/config.yml b/admin/config.yml index 21458be..d05a887 100644 --- a/admin/config.yml +++ b/admin/config.yml @@ -6,7 +6,7 @@ backend: # Uncomment below to enable drafts # publish_mode: editorial_workflow -media_folder: "_assets/img" # Media files will be stored in the repo under static/images/uploads +media_folder: "/assets/img" # Media files will be stored in the repo under static/images/uploads public_folder: "/assets/img" # The src attribute for uploaded media will begin with /images/uploads collections: @@ -17,7 +17,7 @@ collections: folder: words path: "{{tags}}/{{word}}" create: true - media_folder: "_assets/img" + media_folder: "/assets/img" public_folder: "/assets/img" # adding a nested object will show the collection folder structure fields: # The fields for each document, usually in front matter diff --git a/public/A/a-b_testing/index.html b/public/A/a-b_testing/index.html deleted file mode 100644 index 1a6c016..0000000 --- a/public/A/a-b_testing/index.html +++ /dev/null @@ -1,12 +0,0 @@ - - -
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یک روش آماری است که به کمک آن می‌توان یک یا دو شیوه (تکنیک) را مقایسه کرد. به طور معمول یک وظیفه لازم در برابر رقیب جدید است که هدف آن تنها تعیین عملکرد بهتر نیست؛ بلکه برای اینکه درک کنیم آیا این تفاوت از نظر آماری معنادار است یا خیر نیز کاربرد دارد.

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آزمایش A/B معمولاً تنها دو شیوه (تکنیک) را با استفاده از یک اندازه گیری در نظر می‌گیرد ، اما می‌تواند برای هر تعداد محدودی از تکنیک‌ها و اقدامات استفاده شود.

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کسری از پیش‌بینی‌های درست یک مدل طبقه‌بندی است که در طبقه‌بندی چند کلاسه به صورت زیر تعریف می‌شود:

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کارایی = تعداد کل نمونه‌ها/پیش‌بینی‌های درست

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در طبقه‌بند دودویی کارایی به صورت زیر تعریف می‌شود :

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ساز و کاری که توسط آن عامل بین حالت‌های محیط تغییر می‌کند.عامل عملش را با استفاده از خط مشی انتخاب می‌کند.

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یک روش آماری است که به کمک آن می‌توان یک یا دو شیوه (تکنیک) را مقایسه کرد. به طور معمول یک وظیفه لازم در برابر رقیب جدید است که هدف آن تنها تعیین عملکرد بهتر نیست؛ بلکه برای اینکه درک کنیم آیا این تفاوت از نظر آماری معنادار است یا خیر نیز کاربرد دارد.

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آزمایش A/B معمولاً تنها دو شیوه (تکنیک) را با استفاده از یک اندازه گیری در نظر می‌گیرد ، اما می‌تواند برای هر تعداد محدودی از تکنیک‌ها و اقدامات استفاده شود.

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کسری از پیش‌بینی‌های درست یک مدل طبقه‌بندی است که در طبقه‌بندی چند کلاسه به صورت زیر تعریف می‌شود:

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کارایی = تعداد کل نمونه‌ها/پیش‌بینی‌های درست

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در طبقه‌بند دودویی کارایی به صورت زیر تعریف می‌شود :

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مثبت واقعی + منفی واقعی/تعداد کل نمونه‌ها

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ساز و کاری که توسط آن عامل بین حالت‌های محیط تغییر می‌کند.عامل عملش را با استفاده از خط مشی انتخاب می‌کند.

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شاخه‌ای از الگوریتم‌های خوشه‌بندی یا clustering که داده‌ها را در خوشه‌های غیرسلسله‌ مراتبی سازمان‌دهی می‌کند. k-means از پراستفاده‌ترین الگوریتم‌های این شاخه است.

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این کلمه با hierarchical clustering یا خوشه‌بندی سلسله مراتبی متضاد است.

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مرکز خوشه‌هایی که توسط الگوریتم‌های k-means یا k-median مشخص می‌شوند. به عنوان مثال، اگر k برابر ۳ باشد، الگوریتم‌های ‌k-means یا k-median سه مرکز خوشه یا centroid پیدا خواهند کرد.

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داده‌هایی که وضعیت متغیرهای یک مدل را در یک زمان مشخص ذخیره می‌کنند. با استفاده از این داده‌ها،‌ امکان خروجی گرفتن از وزن‌های مدل را در حالی که در نشست‌های (session) مختلف در حال آموزش است فراهم می‌شود. آن‌ها هم‌چنین این اجازه را می‌دهند که فرآیند آموزش پس از مواجه شدن با خطا (مثلا گرفتن سخت‌افزار مورد نیاز آموزش توسط دیگر برنامه‌ها) ادامه پیدا کند. توجه کنید که گراف به خودی خود در نقطه وارسی (checkpoint) ذخیره نمی‌شود.

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مشکلی است که در مساله‌های دسته‌بندی دوتایی (binary classification) پیش می‌آید که در آن تعداد برچسب‌های یک دسته با دیگری تفاوت زیادی دارد. به عنوان مثال، مجموعه داده‌ای مربوط به یک بیماری که در آن ۰.۰۰۰۱ داده‌ها برچسب مثبت و بقیه داده‌ها برچسب منفی دارند دچار مشکل مجموعه داده نامتوازن است؛ اما مجموعه داده‌های مربوط به یک پیشگوی بازی فوتبال که در آن ۰.۵۱ داده‌ها برچسب برد یک تیم و ۰.۴۹ داده‌ها برچسب برد تیم دیگر را دارند دارای این مشکل نیست.

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هر کدام از مجموعه مقادیر ذکر شده که یک برچسب می‌تواند بپذیرد. برای مثال، در یک مدل دسته‌بندی دوتایی‌ که هرزنامه‌ها را تشخیص می‌دهد، «هرزنامه» و «غیر هرزنامه» دو دسته هستند. یا در یک مدل دسته‌بندی چندتایی که نژاد سگ را تشخیص می‌دهد، دسته‌ها پودل، بیگل، پاگ و ... خواهند بود.

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نوعی از مدل‌های یادگیری ماشین که برای تمایز قائل شدن میان دو یا چند دسته مجزا استفاده می‌شوند. به عنوان مثال، یک مدل دسته‌بندی پردازش زبان طبیعی می‌تواند تشخیص دهد که جمله ورودی به کدام یک از زبان‌های فرانسوی، اسپانیایی یا ایتالیایی تعلق دارد. این لغت را با مدل‌های وایازشی (regression model) مقایسه کنید.

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شاخه‌ای از الگوریتم‌های خوشه‌بندی یا clustering که داده‌ها را در خوشه‌های غیرسلسله‌ مراتبی سازمان‌دهی می‌کند. k-means از پراستفاده‌ترین الگوریتم‌های این شاخه است.

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این کلمه با hierarchical clustering یا خوشه‌بندی سلسله مراتبی متضاد است.

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مرکز خوشه‌هایی که توسط الگوریتم‌های k-means یا k-median مشخص می‌شوند. به عنوان مثال، اگر k برابر ۳ باشد، الگوریتم‌های ‌k-means یا k-median سه مرکز خوشه یا centroid پیدا خواهند کرد.

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داده‌هایی که وضعیت متغیرهای یک مدل را در یک زمان مشخص ذخیره می‌کنند. با استفاده از این داده‌ها،‌ امکان خروجی گرفتن از وزن‌های مدل را در حالی که در نشست‌های (session) مختلف در حال آموزش است فراهم می‌شود. آن‌ها هم‌چنین این اجازه را می‌دهند که فرآیند آموزش پس از مواجه شدن با خطا (مثلا گرفتن سخت‌افزار مورد نیاز آموزش توسط دیگر برنامه‌ها) ادامه پیدا کند. توجه کنید که گراف به خودی خود در نقطه وارسی (checkpoint) ذخیره نمی‌شود.

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مشکلی است که در مساله‌های دسته‌بندی دوتایی (binary classification) پیش می‌آید که در آن تعداد برچسب‌های یک دسته با دیگری تفاوت زیادی دارد. به عنوان مثال، مجموعه داده‌ای مربوط به یک بیماری که در آن ۰.۰۰۰۱ داده‌ها برچسب مثبت و بقیه داده‌ها برچسب منفی دارند دچار مشکل مجموعه داده نامتوازن است؛ اما مجموعه داده‌های مربوط به یک پیشگوی بازی فوتبال که در آن ۰.۵۱ داده‌ها برچسب برد یک تیم و ۰.۴۹ داده‌ها برچسب برد تیم دیگر را دارند دارای این مشکل نیست.

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هر کدام از مجموعه مقادیر ذکر شده که یک برچسب می‌تواند بپذیرد. برای مثال، در یک مدل دسته‌بندی دوتایی‌ که هرزنامه‌ها را تشخیص می‌دهد، «هرزنامه» و «غیر هرزنامه» دو دسته هستند. یا در یک مدل دسته‌بندی چندتایی که نژاد سگ را تشخیص می‌دهد، دسته‌ها پودل، بیگل، پاگ و ... خواهند بود.

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نوعی از مدل‌های یادگیری ماشین که برای تمایز قائل شدن میان دو یا چند دسته مجزا استفاده می‌شوند. به عنوان مثال، یک مدل دسته‌بندی پردازش زبان طبیعی می‌تواند تشخیص دهد که جمله ورودی به کدام یک از زبان‌های فرانسوی، اسپانیایی یا ایتالیایی تعلق دارد. این لغت را با مدل‌های وایازشی (regression model) مقایسه کنید.

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- - True Negative (TN) - -

مثالی که در آن مدل کلاس منفی را به درستی پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص اسپم نیست و آن پیام واقعا اسپم نبوده است.

- - -
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مثالی که در آن مدل به درستی کلاس مثبت را پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص هرزنامه است و آن پیام واقعا هرزنامه بوده است.

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مثالی که در آن مدل کلاس منفی را به درستی پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص اسپم نیست و آن پیام واقعا اسپم نبوده است.

-
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- -

مثالی که در آن مدل به درستی کلاس مثبت را پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص هرزنامه است و آن پیام واقعا هرزنامه بوده است.

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- -
- -
\ No newline at end of file diff --git a/public/U/index.html b/public/U/index.html deleted file mode 100644 index b8c3c06..0000000 --- a/public/U/index.html +++ /dev/null @@ -1,38 +0,0 @@ -
- - unawareness_(to_a_sensitive_attribute) - -

testing U!

- - -
- - underfitting - - - -
- - unlabeled_example - - - -
- - unsupervised_machine_learning - - - -
- - upweighting - - - -
- - user_matrix - - - -
\ No newline at end of file diff --git a/public/U/unawareness_(to_a_sensitive_attribute)/index.html b/public/U/unawareness_(to_a_sensitive_attribute)/index.html deleted file mode 100644 index 8414200..0000000 --- a/public/U/unawareness_(to_a_sensitive_attribute)/index.html +++ /dev/null @@ -1,11 +0,0 @@ - - -
- -

testing U!

-
- -
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- -
- -
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- -
- -
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- -
- -
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- -
- -
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- -
- -
\ No newline at end of file diff --git a/public/V/index.html b/public/V/index.html deleted file mode 100644 index 0f56d11..0000000 --- a/public/V/index.html +++ /dev/null @@ -1,19 +0,0 @@ -
- - validation - - - -
- - validation_set - - - -
- - vanishing_gradient_problem - - - -
\ No newline at end of file diff --git a/public/V/validation/index.html b/public/V/validation/index.html deleted file mode 100644 index bda3ecb..0000000 --- a/public/V/validation/index.html +++ /dev/null @@ -1,10 +0,0 @@ - - -
- -
- -
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- -
- -
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- -
- -
\ No newline at end of file diff --git a/public/W/index.html b/public/W/index.html deleted file mode 100644 index 4a0032c..0000000 --- a/public/W/index.html +++ /dev/null @@ -1,31 +0,0 @@ -
- - wasserstein_loss - - - -
- - weight - - - -
- - weighted_alternating_least_squares_(wals) - - - -
- - wide_model - - - -
- - width - - - -
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- -
- -
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- -
- -
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- -
- -
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- -
- -
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- -
- -
\ No newline at end of file diff --git a/public/X/index.html b/public/X/index.html deleted file mode 100644 index 2407bd3..0000000 --- a/public/X/index.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/public/Y/index.html b/public/Y/index.html deleted file mode 100644 index 2407bd3..0000000 --- a/public/Y/index.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/public/Z/index.html b/public/Z/index.html deleted file mode 100644 index 2407bd3..0000000 --- a/public/Z/index.html +++ /dev/null @@ -1 +0,0 @@ - \ No newline at end of file diff --git a/public/admin/config.yml b/public/admin/config.yml deleted file mode 100644 index b94d13d..0000000 --- a/public/admin/config.yml +++ /dev/null @@ -1,156 +0,0 @@ -backend: - # name: test-repo - name: git-gateway - branch: develop # Branch to update (optional; defaults to master) - -# Uncomment below to enable drafts -# publish_mode: editorial_workflow - -media_folder: "_assets/img" # Media files will be stored in the repo under static/images/uploads -public_folder: "/assets/img" # The src attribute for uploaded media will begin with /images/uploads - -collections: - - name: word - label: Word - identifier_field: word - label_singular: "Word" - folder: words - path: "{{tags}}/{{word}}" - create: true - # adding a nested object will show the collection folder structure - fields: # The fields for each document, usually in front matter - - { - label: "_layout", - name: "layout", - widget: "string", - default: "word", - required: false, - } - - { label: "Word", name: "word", widget: "string", hint: "e.g. accuracy" } - - { - label: "Translation", - name: "translation", - widget: "string", - hint: "مثلا، کارایی", - } - - label: "Tags" - name: "tags" - widget: "select" - multiple: true - options: - [ - "A", - "B", - "C", - "D", - "E", - "F", - "G", - "H", - "I", - "J", - "K", - "L", - "M", - "N", - "O", - "P", - "Q", - "R", - "S", - "T", - "U", - "V", - "W", - "X", - "Y", - "Z", - ] - - # - label: "Tags" - # name: "tags" - # widget: "relation" - # collection: "authors" - # searchFields: ["name", "github"] - # valueField: "name" - - - { label: "Body", name: "body", widget: "markdown" } - view_filters: - - label: "A" - field: tags - pattern: "A" - - label: "B" - field: tags - pattern: "B" - - label: "C" - field: tags - pattern: "C" - - label: "D" - field: tags - pattern: "D" - - label: "E" - field: tags - pattern: "E" - - label: "F" - field: tags - pattern: "F" - - label: "G" - field: tags - pattern: "G" - - label: "H" - field: tags - pattern: "H" - - label: "I" - field: tags - pattern: "I" - - label: "J" - field: tags - pattern: "J" - - label: "K" - field: tags - pattern: "K" - - label: "L" - field: tags - pattern: "L" - - label: "M" - field: tags - pattern: "M" - - label: "N" - field: tags - pattern: "N" - - label: "O" - field: tags - pattern: "O" - - label: "P" - field: tags - pattern: "P" - - label: "Q" - field: tags - pattern: "Q" - - label: "R" - field: tags - pattern: "R" - - label: "S" - field: tags - pattern: "S" - - label: "T" - field: tags - pattern: "T" - - label: "U" - field: tags - pattern: "U" - - label: "V" - field: tags - pattern: "V" - - label: "W" - field: tags - pattern: "W" - - label: "X" - field: tags - pattern: "X" - - label: "Y" - field: tags - pattern: "Y" - - label: "Z" - field: tags - pattern: "Z" diff --git a/public/admin/index.html b/public/admin/index.html deleted file mode 100644 index 302c137..0000000 --- a/public/admin/index.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - - - Content Manager - - - - - - - diff --git a/public/assets/index.css b/public/assets/index.css deleted file mode 100644 index 98422ae..0000000 --- a/public/assets/index.css +++ /dev/null @@ -1,157 +0,0 @@ -@import "https://unpkg.com/modern-normalize@0.6.0/modern-normalize.css"; - -html, -body { - scroll-behavior: smooth; -} - -*, -*::before, -*::after { - font-family: Vazir; -} - -body { - display: grid; - grid-template-columns: auto 10vw; - grid-template-rows: 400px auto; - grid-template-areas: - "header header" - "main aside"; -} - -header { - grid-area: header; - height: 400px; - width: 100%; - background-color: #f9fafb; - border-bottom: 1px solid #d2d6dc; -} - -a { - color: inherit; - text-decoration: none; -} - -a:visited { - text-decoration: none; - color: inherit; -} - -.letter-section { - border: 1px solid #ccc; - padding: 1rem; - margin: 1rem; - scroll-margin: 1em; -} - -main { - grid-area: main; -} - -#search { - width: 100%; - height: 15vh; - display: flex; - flex-direction: column; - justify-content: space-evenly; - align-items: center; -} - -#search span { - display: block; - width: 80%; - direction: rtl; - margin-bottom: 0.4em; -} - -#search input#query { - width: 80%; - height: 40%; - border-radius: 5px; - - line-height: 20px; - color: #24292e; - vertical-align: middle; - background-color: #fff; - background-repeat: no-repeat; - background-position: right 8px center; - border: 1px solid #d1d5da; - border-radius: 3px; - outline: none; - box-shadow: inset 0 1px 2px rgba(27, 31, 35, 0.075); -} - -details { - border: 1px solid #d2d6dc; - box-sizing: border-box; - border-radius: 4px; - padding: 0.5em 0.5em 0; - margin: 10px; - direction: rtl; - - background-color: #eff3f6; - background-image: linear-gradient(-180deg, #fafbfc 0%, #eff3f6 90%); -} - -details > summary { - list-style: none; - font-weight: bold; - margin: -0.5em -0.5em 0; - padding: 0.5em; - direction: ltr; - /* color: #3f83f8; */ - color: #24292e; - display: flex; - flex-direction: row; - justify-content: space-between; - align-items: center; -} - -details > summary svg { - width: 25px; - height: 25px; - margin-left: 1em; - color: #ccc; -} - -details > summary::-webkit-details-marker { - display: none; -} - -details[open] { - padding: 0.5em; -} - -details[open] > summary { - border-bottom: 1px solid #aaa; - margin-bottom: 0.5em; -} - -details[open] > summary svg { - transform: rotate(90deg); - transition: transform 0.1s ease-in; -} - -details > summary span.translation { - direction: rtl; - unicode-bidi: isolate; -} - -aside { - grid-area: aside; -} - -aside > ul { - list-style: none; - border-left: 6px solid #ccc; - padding: 0; - height: max-content; - position: sticky; - top: 1em; -} - -aside > ul > li { - padding-left: 1em; - margin-bottom: 5px; -} diff --git a/public/assets/index.js b/public/assets/index.js deleted file mode 100644 index a6d560d..0000000 --- a/public/assets/index.js +++ /dev/null @@ -1,35 +0,0 @@ -const onLoad = () => { - console.log("Load"); - const searchbar = document.querySelector("#query"); - const letterSections = document.querySelectorAll(".letter-section"); - const words = document.querySelectorAll(".word"); - - searchbar.addEventListener("input", (event) => { - const query = event.target.value.trim(); - - if (!query.length) words.forEach((w) => (w.style.display = "block")); - - words.forEach( - (w) => - (w.style.display = w - .querySelector("a") - .textContent.toLowerCase() - .includes(query) - ? "block" - : "none") - ); - - letterSections.forEach((ls) => { - if ( - [...ls.querySelectorAll(".word")].every( - (w) => w.style.display === "none" - ) - ) - ls.style.display = "none"; - else - ls.style.display = "block"; - }); - }); -}; - -window.onload = onLoad; diff --git a/public/assets/index2.css b/public/assets/index2.css deleted file mode 100644 index a0b32fd..0000000 --- a/public/assets/index2.css +++ /dev/null @@ -1,152 +0,0 @@ -@import "https://unpkg.com/modern-normalize@0.6.0/modern-normalize.css"; - -html, -body { - scroll-behavior: smooth; -} - -*, -*::before, -*::after { - font-family: Vazir; -} - -body { - display: grid; - grid-template-columns: auto 10vw; - grid-template-rows: 400px auto; - grid-template-areas: - "header header" - "main aside"; -} - -header { - grid-area: header; - height: 400px; - width: 100%; - background-color: #f9fafb; - border-bottom: 1px solid #d2d6dc; -} - -a { - color: inherit; - text-decoration: none; -} - -a:visited { - text-decoration: none; - color: inherit; -} - -.letter-section { - border: 1px solid #ccc; - padding: 1rem; - margin: 1rem; - scroll-margin: 1em; -} - -main { - grid-area: main; -} - -#search { - width: 100%; - height: 15vh; - display: flex; - flex-direction: column; - justify-content: space-evenly; - align-items: center; -} - -#search span { - display: block; - width: 80%; - direction: rtl; - margin-bottom: 0.4em; -} - -#search input#query { - width: 80%; - height: 40%; - border-radius: 5px; - - line-height: 20px; - color: #24292e; - vertical-align: middle; - background-color: #fff; - background-repeat: no-repeat; - background-position: right 8px center; - border: 1px solid #d1d5da; - border-radius: 3px; - outline: none; - box-shadow: inset 0 1px 2px rgba(27, 31, 35, 0.075); -} - -#word { - width: 400px; - max-height: 400px; - height: auto; - background: white; - display: grid; - grid-template: 4em 300px 3em / 1fr; - /* box-shadow: 0 0 25px -20px #607d8b; */ - margin: 4em; -} - -#header { - text-align: center; - display: flex; - flex-direction: column-reverse; - justify-content: center; - align-items: flex-start; - padding: 2em 0; - margin: 0 auto 0; - direction: rtl; - text-align: center; -} - -h2 { - margin: 0; - font-size: 2em; -} - -h3 { - margin: 0; - font-size: 0.8em; - font-weight: 300; -} - -#content { - padding: 2em; -} - -p { - direction: rtl; - text-align: start; - background: white; - line-height: 1.4; -} - -#footer { - width: 100%; - height: 3em; - background: #ccc; -} - -aside { - grid-area: aside; -} - -aside > ul { - list-style: none; - border-left: 6px solid #ccc; - padding: 0; - height: max-content; - position: sticky; - top: 1em; -} - -aside > ul > li { - padding-left: 1em; - margin-bottom: 5px; -} diff --git a/public/assets/word.css b/public/assets/word.css deleted file mode 100644 index 8110318..0000000 --- a/public/assets/word.css +++ /dev/null @@ -1,86 +0,0 @@ -@import "https://unpkg.com/modern-normalize@0.6.0/modern-normalize.css"; -@import "https://cdn.jsdelivr.net/gh/rastikerdar/vazir-font@v26.0.2/dist/font-face.css"; - -html, -body { - scroll-behavior: smooth; -} - -*, -*::before, -*::after { - font-family: Vazir; -} - -body { - width: 100%; - height: 100%; - display: grid; - place-items: center; - background: #efefef42; -} - -#word { - width: 400px; - min-height: 400px; - height: auto; - background: white; - display: flex; - flex-direction: column; - justify-content: space-between; - align-items: center; - box-shadow: 0 0 25px -20px #607d8b; -} - -#header { - text-align: center; - display: flex; - flex-direction: column-reverse; - justify-content: center; - align-items: flex-start; - padding: 2em 0; - margin: 0 auto 0; - direction: rtl; - text-align: center; -} - -h2 { - margin: 0; - font-size: 2em; -} - -h3 { - margin: 0; - font-size: 0.8em; - font-weight: 300; -} - -#content { - padding: 2em; -} - -p { - direction: rtl; - text-align: start; - background: white; - line-height: 1.4; - text-align: justify; -} - -p a { - color: #2f2f2f; - text-decoration: wavy; - text-decoration-color: #ccc; - text-decoration-line: underline; - /* text-decoration-color: currentcolor; */ -} - -/* p a:visited { - text-decoration-color: inherit; -} */ - -#footer { - width: 100%; - height: 3em; - background: #ccc; -} diff --git a/public/assets/words.css b/public/assets/words.css deleted file mode 100644 index 326cdca..0000000 --- a/public/assets/words.css +++ /dev/null @@ -1,66 +0,0 @@ -@import "https://unpkg.com/modern-normalize@0.6.0/modern-normalize.css"; -@import "https://cdn.jsdelivr.net/gh/rastikerdar/vazir-font@v26.0.2/dist/font-face.css"; - -html, -body { - scroll-behavior: smooth; -} - -*, -*::before, -*::after { - font-family: Vazir; -} - -body { -} - -details { - border: 1px solid #d2d6dc; - box-sizing: border-box; - border-radius: 4px; - padding: 0.5em 0.5em 0; - margin: 10px; - direction: rtl; - - background-color: #eff3f6; - background-image: linear-gradient(-180deg, #fafbfc 0%, #eff3f6 90%); -} - -details > summary { - list-style: none; - font-weight: bold; - margin: -0.5em -0.5em 0; - padding: 0.5em; - direction: ltr; - /* color: #3f83f8; */ - color: #24292e; - display: flex; - flex-direction: row; - justify-content: space-between; - align-items: center; -} - -details > summary > svg { - width: 25px; - height: 25px; - color: #ccc; -} - -details > summary::-webkit-details-marker { - display: none; -} - -details[open] { - padding: 0.5em; -} - -details[open] > summary { - border-bottom: 1px solid #aaa; - margin-bottom: 0.5em; -} - -details[open] > summary > svg { - transform: rotate(90deg); - transition: transform 0.1s ease-in; -} diff --git a/public/index.html b/public/index.html deleted file mode 100644 index f899117..0000000 --- a/public/index.html +++ /dev/null @@ -1,4671 +0,0 @@ - - - - - - Document - - - - - - - -
- -
- -
- A
- - activation_function - - - - - - - - -
- - active_learning - - - - - - - - -
- - adagrad - - - - - - - - -
- - agent - - - - - - - - -
- - agglomerative_clustering - - - - - - - - -
- - ar - - - - - - - - -
- - area_under_the_pr_curve - - - - - - - - -
- - area_under_the_roc_curve - - - - - - - - -
- - artificial_general_intelligence - - - - - - - - -
- - artificial_intelligence - - - - - - - - -
- - attribute - - - - - - - - -
- - auc_(area_under_the_roc_curve) - - - - - - - - -
- - augmented_reality - - - - - - - - -
- - automation_bias - - - - - - - - -
- - average_precision - - - - - - - - -
- - A/B Testing - - آزمایش A/B - - - - - -

یک روش آماری است که به کمک آن می‌توان یک یا دو شیوه (تکنیک) را مقایسه کرد. به طور معمول یک وظیفه لازم در برابر رقیب جدید است که هدف آن تنها تعیین عملکرد بهتر نیست؛ بلکه برای اینکه درک کنیم آیا این تفاوت از نظر آماری معنادار است یا خیر نیز کاربرد دارد.

-

آزمایش A/B معمولاً تنها دو شیوه (تکنیک) را با استفاده از یک اندازه گیری در نظر می‌گیرد ، اما می‌تواند برای هر تعداد محدودی از تکنیک‌ها و اقدامات استفاده شود.

- -
- - accuracy - - کارایی - - - - - -

کسری از پیش‌بینی‌های درست یک مدل طبقه‌بندی است که در طبقه‌بندی چند کلاسه به صورت زیر تعریف می‌شود:

-

کارایی = تعداد کل نمونه‌ها/پیش‌بینی‌های درست

-

در طبقه‌بند دودویی کارایی به صورت زیر تعریف می‌شود :

-

مثبت واقعی + منفی واقعی/تعداد کل نمونه‌ها

- -
- - action - - عمل - - - - - -

ساز و کاری که توسط آن عامل بین حالت‌های محیط تغییر می‌کند.عامل عملش را با استفاده از خط مشی انتخاب می‌کند.

- -
- B
- - backpropagation - - - - - - - - -
- - bag_of_words - - - - - - - - -
- - baseline - - - - - - - - -
- - batch - - - - - - - - -
- - batch_normalization - - - - - - - - -
- - batch_size - - - - - - - - -
- - bayesian_neural_network - - - - - - - - -
- - bellman_equation - - - - - - - - -
- - bias_(ethics-fairness) - - - - - - - - -
- - bias_(math) - - - - - - - - -
- - bigram - - - - - - - - -
- - binary_classification - - - - - - - - -
- - binning - - - - - - - - -
- - boosting - - - - - - - - -
- - bounding_box - - - - - - - - -
- - broadcasting - - - - - - - - -
- - bucketing - - - - - - - - -
- C
- - calibration_layer - - - - - - - - -
- - candidate_generation - - - - - - - - -
- - candidate_sampling - - - - - - - - -
- - categorical_data - - - - - - - - -
- - classification_threshold - - - - - - - - -
- - clipping - - - - - - - - -
- - cloud_tpu - - - - - - - - -
- - clustering - - - - - - - - -
- - co-adaptation - - - - - - - - -
- - collaborative_filtering - - - - - - - - -
- - confirmation_bias - - - - - - - - -
- - confusion_matrix - - - - - - - - -
- - continuous_feature - - - - - - - - -
- - convenience_sampling - - - - - - - - -
- - convergence - - - - - - - - -
- - convex_function - - - - - - - - -
- - convex_optimization - - - - - - - - -
- - convex_set - - - - - - - - -
- - convolution - - - - - - - - -
- - convolutional_filter - - - - - - - - -
- - convolutional_layer - - - - - - - - -
- - convolutional_neural_network - - - - - - - - -
- - convolutional_operation - - - - - - - - -
- - cost - - - - - - - - -
- - counterfactual_fairness - - - - - - - - -
- - coverage_bias - - - - - - - - -
- - crash_blossom - - - - - - - - -
- - critic - - - - - - - - -
- - cross-entropy - - - - - - - - -
- - cross-validation - - - - - - - - -
- - custom_estimator - - - - - - - - -
- - centroid-based_clustering - - - - - - - -

شاخه‌ای از الگوریتم‌های خوشه‌بندی یا clustering که داده‌ها را در خوشه‌های غیرسلسله‌ مراتبی سازمان‌دهی می‌کند. k-means از پراستفاده‌ترین الگوریتم‌های این شاخه است.

-

این کلمه با hierarchical clustering یا خوشه‌بندی سلسله مراتبی متضاد است.

- -
- - centroid - - مرکز خوشه - - - - - -

مرکز خوشه‌هایی که توسط الگوریتم‌های k-means یا k-median مشخص می‌شوند. به عنوان مثال، اگر k برابر ۳ باشد، الگوریتم‌های ‌k-means یا k-median سه مرکز خوشه یا centroid پیدا خواهند کرد.

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- - checkpoint - - نقطه وارسی - - - - - -

داده‌هایی که وضعیت متغیرهای یک مدل را در یک زمان مشخص ذخیره می‌کنند. با استفاده از این داده‌ها،‌ امکان خروجی گرفتن از وزن‌های مدل را در حالی که در نشست‌های (session) مختلف در حال آموزش است فراهم می‌شود. آن‌ها هم‌چنین این اجازه را می‌دهند که فرآیند آموزش پس از مواجه شدن با خطا (مثلا گرفتن سخت‌افزار مورد نیاز آموزش توسط دیگر برنامه‌ها) ادامه پیدا کند. توجه کنید که گراف به خودی خود در نقطه وارسی (checkpoint) ذخیره نمی‌شود.

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- - class-imbalanced_dataset - - مجموعه داده نامتوازن - - - - - -

مشکلی است که در مساله‌های دسته‌بندی دوتایی (binary classification) پیش می‌آید که در آن تعداد برچسب‌های یک دسته با دیگری تفاوت زیادی دارد. به عنوان مثال، مجموعه داده‌ای مربوط به یک بیماری که در آن ۰.۰۰۰۱ داده‌ها برچسب مثبت و بقیه داده‌ها برچسب منفی دارند دچار مشکل مجموعه داده نامتوازن است؛ اما مجموعه داده‌های مربوط به یک پیشگوی بازی فوتبال که در آن ۰.۵۱ داده‌ها برچسب برد یک تیم و ۰.۴۹ داده‌ها برچسب برد تیم دیگر را دارند دارای این مشکل نیست.

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- - class - - دسته - - - - - -

هر کدام از مجموعه مقادیر ذکر شده که یک برچسب می‌تواند بپذیرد. برای مثال، در یک مدل دسته‌بندی دوتایی‌ که هرزنامه‌ها را تشخیص می‌دهد، «هرزنامه» و «غیر هرزنامه» دو دسته هستند. یا در یک مدل دسته‌بندی چندتایی که نژاد سگ را تشخیص می‌دهد، دسته‌ها پودل، بیگل، پاگ و ... خواهند بود.

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- - classification_model - - مدل دسته‌بندی - - - - - -

نوعی از مدل‌های یادگیری ماشین که برای تمایز قائل شدن میان دو یا چند دسته مجزا استفاده می‌شوند. به عنوان مثال، یک مدل دسته‌بندی پردازش زبان طبیعی می‌تواند تشخیص دهد که جمله ورودی به کدام یک از زبان‌های فرانسوی، اسپانیایی یا ایتالیایی تعلق دارد. این لغت را با مدل‌های وایازشی (regression model) مقایسه کنید.

- -
- D
- - data_analysis - - - - - - - - -
- - data_augmentation - - - - - - - - -
- - data_set_or_dataset - - - - - - - - -
- - dataframe - - - - - - - - -
- - dataset_api_(tf.data) - - - - - - - - -
- - decision_boundary - - - - - - - - -
- - decision_threshold - - - - - - - - -
- - decision_tree - - - - - - - - -
- - deep_model - - - - - - - - -
- - deep_neural_network - - - - - - - - -
- - deep_q-network_(dqn) - - - - - - - - -
- - demographic_parity - - - - - - - - -
- - dense_feature - - - - - - - - -
- - dense_layer - - - - - - - - -
- - depth - - - - - - - - -
- - depthwise_separable_convolutional_neural_network_(sepcnn) - - - - - - - - -
- - device - - - - - - - - -
- - dimension_reduction - - - - - - - - -
- - dimensions - - - - - - - - -
- - discrete_feature - - - - - - - - -
- - discriminative_model - - - - - - - - -
- - discriminator - - - - - - - - -
- - disparate_impact - - - - - - - - -
- - disparate_treatment - - - - - - - - -
- - divisive_clustering - - - - - - - - -
- - downsampling - - - - - - - - -
- - dqn - - - - - - - - -
- - dropout_regularization - - - - - - - - -
- - dynamic_model - - - - - - - - -
- E
- - eager_execution - - - - - - - - -
- - early_stopping - - - - - - - - -
- - embedding_space - - - - - - - - -
- - embeddings - - - - - - - - -
- - empirical_risk_minimization_(erm) - - - - - - - - -
- - ensemble - - - - - - - - -
- - environment - - - - - - - - -
- - episode - - - - - - - - -
- - epoch - - - - - - - - -
- - epsilon_greedy_policy - - - - - - - - -
- - equality_of_opportunity - - - - - - - - -
- - equalized_odds - - - - - - - - -
- - estimator - - - - - - - - -
- - example - - - - - - - - -
- - experience_replay - - - - - - - - -
- - experimenter's_bias - - - - - - - - -
- - exploding_gradient_problem - - - - - - - - -
- F
- - fairness_constraint - - - - - - - - -
- - fairness_metric - - - - - - - - -
- - false_negative_(fn) - - - - - - - - -
- - false_positive_(fp) - - - - - - - - -
- - false_positive_rate_(fpr) - - - - - - - - -
- - feature - - - - - - - - -
- - feature_column_(tf.feature_column) - - - - - - - - -
- - feature_cross - - - - - - - - -
- - feature_engineering - - - - - - - - -
- - feature_extraction - - - - - - - - -
- - feature_set - - - - - - - - -
- - feature_spec - - - - - - - - -
- - feature_vector - - - - - - - - -
- - federated_learning - - - - - - - - -
- - feedback_loop - - - - - - - - -
- - feedforward_neural_network_(ffn) - - - - - - - - -
- - few-shot_learning - - - - - - - - -
- - fine_tuning - - - - - - - - -
- - forget_gate - - - - - - - - -
- - full_softmax - - - - - - - - -
- - fully_connected_layer - - - - - - - - -
- G
- - gan - - - - - - - - -
- - generalization - - - - - - - - -
- - generalization_curve - - - - - - - - -
- - generalized_linear_model - - - - - - - - -
- - generative_adversarial_network_(gan) - - - - - - - - -
- - generative_model - - - - - - - - -
- - generator - - - - - - - - -
- - gradient - - - - - - - - -
- - gradient_clipping - - - - - - - - -
- - gradient_descent - - - - - - - - -
- - graph - - - - - - - - -
- - graph_execution - - - - - - - - -
- - greedy_policy - - - - - - - - -
- - ground_truth - - - - - - - - -
- - group_attribution_bias - - - - - - - - -
- H
- - hashing - - - - - - - - -
- - heuristic - - - - - - - - -
- - hidden_layer - - - - - - - - -
- - hierarchical_clustering - - - - - - - - -
- - hinge_loss - - - - - - - - -
- - holdout_data - - - - - - - - -
- - hyperparameter - - - - - - - - -
- - hyperplane - - - - - - - - -
- I
- - i.i.d. - - - - - - - - -
- - image_recognition - - - - - - - - -
- - imbalanced_dataset - - - - - - - - -
- - implicit_bias - - - - - - - - -
- - in-group_bias - - - - - - - - -
- - incompatibility_of_fairness_metrics - - - - - - - - -
- - independently_and_identically_distributed_(i.i.d) - - - - - - - - -
- - individual_fairness - - - - - - - - -
- - inference - - - - - - - - -
- - input_function - - - - - - - - -
- - input_layer - - - - - - - - -
- - instance - - - - - - - - -
- - inter-rater_agreement - - - - - - - - -
- - interpretability - - - - - - - - -
- - intersection_over_union_(iou) - - - - - - - - -
- - iou - - - - - - - - -
- - item_matrix - - - - - - - - -
- - items - - - - - - - - -
- - iteration - - - - - - - - -
- J
- K
- - k-means - - - - - - - - -
- - k-median - - - - - - - - -
- - keras - - - - - - - - -
- - kernel_support_vector_machines_(ksvms) - - - - - - - - -
- - keypoints - - - - - - - - -
- L
- - l1_loss - - - - - - - - -
- - l1_regularization - - - - - - - - -
- - l2_loss - - - - - - - - -
- - l2_regularization - - - - - - - - -
- - label - - - - - - - - -
- - labeled_example - - - - - - - - -
- - lambda - - - - - - - - -
- - landmarks - - - - - - - - -
- - layer - - - - - - - - -
- - layers_api_(tf.layers) - - - - - - - - -
- - learning_rate - - - - - - - - -
- - least_squares_regression - - - - - - - - -
- - linear_model - - - - - - - - -
- - linear_regression - - - - - - - - -
- - log-odds - - - - - - - - -
- - log_loss - - - - - - - - -
- - logistic_regression - - - - - - - - -
- - logits - - - - - - - - -
- - long_short-term_memory_(lstm) - - - - - - - - -
- - loss - - - - - - - - -
- - loss_curve - - - - - - - - -
- - loss_surface - - - - - - - - -
- - lstm - - - - - - - - -
- M
- - machine_learning - - - - - - - - -
- - majority_class - - - - - - - - -
- - markov_decision_process_(mdp) - - - - - - - - -
- - markov_property - - - - - - - - -
- - matplotlib - - - - - - - - -
- - matrix_factorization - - - - - - - - -
- - mean_absolute_error_(mae) - - - - - - - - -
- - mean_squared_error_(mse) - - - - - - - - -
- - metric - - - - - - - - -
- - metrics_api_(tf.metrics) - - - - - - - - -
- - mini-batch - - - - - - - - -
- - mini-batch_stochastic_gradient_descent_(sgd) - - - - - - - - -
- - minimax_loss - - - - - - - - -
- - minority_class - - - - - - - - -
- - ml - - - - - - - - -
- - mnist - - - - - - - - -
- - model - - - - - - - - -
- - model_capacity - - - - - - - - -
- - model_function - - - - - - - - -
- - model_training - - - - - - - - -
- - momentum - - - - - - - - -
- - multi-class_classification - - - - - - - - -
- - multi-class_logistic_regression - - - - - - - - -
- - multinomial_classification - - - - - - - - -
- N
- - n-gram - - - - - - - - -
- - nan_trap - - - - - - - - -
- - natural_language_understanding - - - - - - - - -
- - negative_class - - - - - - - - -
- - neural_network - - - - - - - - -
- - neuron - - - - - - - - -
- - nlu - - - - - - - - -
- - node_(neural_network) - - - - - - - - -
- - node_(tensorflow_graph) - - - - - - - - -
- - noise - - - - - - - - -
- - non-response_bias - - - - - - - - -
- - normalization - - - - - - - - -
- - numerical_data - - - - - - - - -
- - numpy - - - - - - - - -
- O
- - objective - - - - - - - - -
- - objective_function - - - - - - - - -
- - offline_inference - - - - - - - - -
- - one-hot_encoding - - - - - - - - -
- - one-shot_learning - - - - - - - - -
- - one-vs.-all - - - - - - - - -
- - online_inference - - - - - - - - -
- - operation_(op) - - - - - - - - -
- - optimizer - - - - - - - - -
- - out-group_homogeneity_bias - - - - - - - - -
- - outliers - - - - - - - - -
- - output_layer - - - - - - - - -
- - overfitting - - - - - - - - -
- P
- - pandas - - - - - - - - -
- - parameter - - - - - - - - -
- - parameter_server_(ps) - - - - - - - - -
- - parameter_update - - - - - - - - -
- - partial_derivative - - - - - - - - -
- - participation_bias - - - - - - - - -
- - partitioning_strategy - - - - - - - - -
- - perceptron - - - - - - - - -
- - performance - - - - - - - - -
- - perplexity - - - - - - - - -
- - pipeline - - - - - - - - -
- - policy - - - - - - - - -
- - pooling - - - - - - - - -
- - positive_class - - - - - - - - -
- - post-processing - - - - - - - - -
- - pr_auc_(area_under_the_pr_curve) - - - - - - - - -
- - pre-trained_model - - - - - - - - -
- - precision-recall_curve - - - - - - - - -
- - precision - - - - - - - - -
- - prediction - - - - - - - - -
- - prediction_bias - - - - - - - - -
- - predictive_parity - - - - - - - - -
- - predictive_rate_parity - - - - - - - - -
- - premade_estimator - - - - - - - - -
- - preprocessing - - - - - - - - -
- - prior_belief - - - - - - - - -
- - proxy_(sensitive_attributes) - - - - - - - - -
- - proxy_labels - - - - - - - - -
- Q
- - q-function - - - - - - - - -
- - q-learning - - - - - - - - -
- - quantile - - - - - - - - -
- - quantile_bucketing - - - - - - - - -
- - quantization - - - - - - - - -
- - queue - - - - - - - - -
- R
- - random_forest - - - - - - - - -
- - random_policy - - - - - - - - -
- - rank_(ordinality) - - - - - - - - -
- - rank_(tensor) - - - - - - - - -
- - rater - - - - - - - - -
- - re-ranking - - - - - - - - -
- - recall - - - - - - - - -
- - recommendation_system - - - - - - - - -
- - rectified_linear_unit_(relu) - - - - - - - - -
- - recurrent_neural_network - - - - - - - - -
- - regression_model - - - - - - - - -
- - regularization - - - - - - - - -
- - regularization_rate - - - - - - - - -
- - reinforcement_learning_(rl) - - - - - - - - -
- - replay_buffer - - - - - - - - -
- - reporting_bias - - - - - - - - -
- - representation - - - - - - - - -
- - return - - - - - - - - -
- - reward - - - - - - - - -
- - ridge_regularization - - - - - - - - -
- - rnn - - - - - - - - -
- - roc_(receiver_operating_characteristic)_curve - - - - - - - - -
- - root_directory - - - - - - - - -
- - root_mean_squared_error_(rmse) - - - - - - - - -
- - rotational_invariance - - - - - - - - -
- S
- - sampling_bias - - - - - - - - -
- - savedmodel - - - - - - - - -
- - saver - - - - - - - - -
- - scalar - - - - - - - - -
- - scaling - - - - - - - - -
- - scikit-learn - - - - - - - - -
- - scoring - - - - - - - - -
- - selection_bias - - - - - - - - -
- - semi-supervised_learning - - - - - - - - -
- - sensitive_attribute - - - - - - - - -
- - sentiment_analysis - - - - - - - - -
- - sequence_model - - - - - - - - -
- - serving - - - - - - - - -
- - session_(tf.session) - - - - - - - - -
- - shape_(tensor) - - - - - - - - -
- - sigmoid_function - - - - - - - - -
- - similarity_measure - - - - - - - - -
- - size_invariance - - - - - - - - -
- - sketching - - - - - - - - -
- - softmax - - - - - - - - -
- - sparse_feature - - - - - - - - -
- - sparse_representation - - - - - - - - -
- - sparse_vector - - - - - - - - -
- - sparsity - - - - - - - - -
- - spatial_pooling - - - - - - - - -
- - squared_hinge_loss - - - - - - - - -
- - squared_loss - - - - - - - - -
- - state-action_value_function - - - - - - - - -
- - state - - - - - - - - -
- - static_model - - - - - - - - -
- - stationarity - - - - - - - - -
- - step - - - - - - - - -
- - step_size - - - - - - - - -
- - stochastic_gradient_descent_(sgd) - - - - - - - - -
- - stride - - - - - - - - -
- - structural_risk_minimization_(srm) - - - - - - - - -
- - subsampling - - - - - - - - -
- - summary - - - - - - - - -
- - supervised_machine_learning - - - - - - - - -
- - synthetic_feature - - - - - - - - -
- T
- - tabular_q-learning - - - - - - - - -
- - target - - - - - - - - -
- - target_network - - - - - - - - -
- - temporal_data - - - - - - - - -
- - tensor - - - - - - - - -
- - tensor_processing_unit_(tpu) - - - - - - - - -
- - tensor_rank - - - - - - - - -
- - tensor_shape - - - - - - - - -
- - tensor_size - - - - - - - - -
- - tensorboard - - - - - - - - -
- - tensorflow - - - - - - - - -
- - tensorflow_playground - - - - - - - - -
- - tensorflow_serving - - - - - - - - -
- - termination_condition - - - - - - - - -
- - test_set - - - - - - - - -
- - tf.example - - - - - - - - -
- - tf.keras - - - - - - - - -
- - time_series_analysis - - - - - - - - -
- - timestep - - - - - - - - -
- - tower - - - - - - - - -
- - tpu - - - - - - - - -
- - tpu_chip - - - - - - - - -
- - tpu_device - - - - - - - - -
- - tpu_master - - - - - - - - -
- - tpu_node - - - - - - - - -
- - tpu_pod - - - - - - - - -
- - tpu_resource - - - - - - - - -
- - tpu_slice - - - - - - - - -
- - tpu_type - - - - - - - - -
- - tpu_worker - - - - - - - - -
- - training - - - - - - - - -
- - training_set - - - - - - - - -
- - trajectory - - - - - - - - -
- - transfer_learning - - - - - - - - -
- - translational_invariance - - - - - - - - -
- - trigram - - - - - - - - -
- - true_positive_rate_(tpr) - - - - - - - - -
- - True Negative (TN) - - منفی واقعی - - - - - -

مثالی که در آن مدل کلاس منفی را به درستی پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص اسپم نیست و آن پیام واقعا اسپم نبوده است.

- -
- - True Positive (TP) - - مثبت واقعی - - - - - -

مثالی که در آن مدل به درستی کلاس مثبت را پیش‌بینی کرده است. به عنوان مثال، این مدل استنباط می‌کند که یک پیام الکترونیکی خاص هرزنامه است و آن پیام واقعا هرزنامه بوده است.

- -
- U
- - unawareness_(to_a_sensitive_attribute) - - - - - - - -

testing U!

- -
- - underfitting - - - - - - - - -
- - unlabeled_example - - - - - - - - -
- - unsupervised_machine_learning - - - - - - - - -
- - upweighting - - - - - - - - -
- - user_matrix - - - - - - - - -
- V
- - validation - - - - - - - - -
- - validation_set - - - - - - - - -
- - vanishing_gradient_problem - - - - - - - - -
- W
- - wasserstein_loss - - - - - - - - -
- - weight - - - - - - - - -
- - weighted_alternating_least_squares_(wals) - - - - - - - - -
- - wide_model - - - - - - - - -
- - width - - - - - - - - -
- X
- Y
- Z
-
- - - - - - \ No newline at end of file