diff --git a/.eleventy.js b/.eleventy.js index a839b03..9a8c7e6 100644 --- a/.eleventy.js +++ b/.eleventy.js @@ -3,10 +3,14 @@ module.exports = function (eleventyConfig) { return collectionApi.getAll(); }); - eleventyConfig.addPassthroughCopy({ _assets: "assets" }); - eleventyConfig.addPassthroughCopy({ _admin: "admin" }); + eleventyConfig.addPassthroughCopy({ assets: "assets" }); + eleventyConfig.addPassthroughCopy({ admin: "admin" }); return { passthroughFileCopy: true, + dir: { + input: "words", + output: "public", + }, }; }; diff --git a/A/A.njk b/A/A.njk deleted file mode 100644 index fa68659..0000000 --- a/A/A.njk +++ /dev/null @@ -1,11 +0,0 @@ - - -{%- for word in collections.A -%} -
- - {{ word.data.word }} - - {{word.templateContent | safe}} - -
-{%- endfor -%} \ No newline at end of file diff --git a/A/a-b_testing.md b/A/a-b_testing.md deleted file mode 100644 index 0913f1e..0000000 --- a/A/a-b_testing.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -layout: word -word: A/B Testing -translation: آزمایش A/B -tags: - - word - - A ---- - -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. diff --git a/A/accuracy.md b/A/accuracy.md deleted file mode 100644 index 6b7106f..0000000 --- a/A/accuracy.md +++ /dev/null @@ -1,16 +0,0 @@ ---- -layout: word -word: accuracy -translation: کارایی -tags: - - A ---- - -کسری از [پیش‌بینی‌های](/P/prediction/) درست یک [مدل طبقه‌بندی](/C/classification_model/) است که در [طبقه‌بندی چند کلاسه](/M/multi-class_classification/) به صورت زیر تعریف می‌شود: - -کارایی = تعداد کل نمونه‌ها/پیش‌بینی‌های درست - -در [طبقه‌بند دودویی](/B/binary_classification/) کارایی به صورت زیر تعریف می‌شود : - -[مثبت واقعی](/T/true_positive_(tp)/) + [منفی واقعی](/T/true_negative_(tn)/)/تعداد کل نمونه‌ها - diff --git a/A/action.md b/A/action.md deleted file mode 100644 index 76e7af6..0000000 --- a/A/action.md +++ /dev/null @@ -1,9 +0,0 @@ ---- -layout: word -word: action -translation: عمل -tags: - - A ---- - -ساز و کاری که توسط آن [عامل](/A/agent/) بین حالت‌های محیط تغییر می‌کند.عامل عملش را با استفاده از خط مشی انتخاب می‌کند. diff --git a/A/activation_function.md b/A/activation_function.md deleted file mode 100644 index f7d1683..0000000 --- a/A/activation_function.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: activation_function -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/active_learning.md b/A/active_learning.md deleted file mode 100644 index c4aa5a6..0000000 --- a/A/active_learning.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: active_learning -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/adagrad.md b/A/adagrad.md deleted file mode 100644 index fa1b1ef..0000000 --- a/A/adagrad.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: adagrad -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/agent.md b/A/agent.md deleted file mode 100644 index d547324..0000000 --- a/A/agent.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: agent -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/agglomerative_clustering.md b/A/agglomerative_clustering.md deleted file mode 100644 index bb70e81..0000000 --- a/A/agglomerative_clustering.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: agglomerative_clustering -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/ar.md b/A/ar.md deleted file mode 100644 index 91153e4..0000000 --- a/A/ar.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: ar -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/area_under_the_pr_curve.md b/A/area_under_the_pr_curve.md deleted file mode 100644 index e32dbad..0000000 --- a/A/area_under_the_pr_curve.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: area_under_the_pr_curve -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/area_under_the_roc_curve.md b/A/area_under_the_roc_curve.md deleted file mode 100644 index b74537e..0000000 --- a/A/area_under_the_roc_curve.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: area_under_the_roc_curve -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/artificial_general_intelligence.md b/A/artificial_general_intelligence.md deleted file mode 100644 index 716456c..0000000 --- a/A/artificial_general_intelligence.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: artificial_general_intelligence -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/artificial_intelligence.md b/A/artificial_intelligence.md deleted file mode 100644 index 8a369a3..0000000 --- a/A/artificial_intelligence.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: artificial_intelligence -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/attribute.md b/A/attribute.md deleted file mode 100644 index a292dcc..0000000 --- a/A/attribute.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: attribute -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/auc_(area_under_the_roc_curve).md b/A/auc_(area_under_the_roc_curve).md deleted file mode 100644 index c6aa892..0000000 --- a/A/auc_(area_under_the_roc_curve).md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: auc_(area_under_the_roc_curve) -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/augmented_reality.md b/A/augmented_reality.md deleted file mode 100644 index 5a12c7c..0000000 --- a/A/augmented_reality.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: augmented_reality -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/automation_bias.md b/A/automation_bias.md deleted file mode 100644 index 8eaa3ac..0000000 --- a/A/automation_bias.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: automation_bias -tags: - - word - - A ---- - \ No newline at end of file diff --git a/A/average_precision.md b/A/average_precision.md deleted file mode 100644 index adb35b7..0000000 --- a/A/average_precision.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: average_precision -tags: - - word - - A ---- - \ No newline at end of file diff --git a/B/B.njk b/B/B.njk deleted file mode 100644 index 2f0ad4d..0000000 --- a/B/B.njk +++ /dev/null @@ -1,11 +0,0 @@ - - -{%- for word in collections.B -%} -
- - {{ word.data.word }} - - {{word.templateContent | safe}} - -
-{%- endfor -%} \ No newline at end of file diff --git a/B/backpropagation.md b/B/backpropagation.md deleted file mode 100644 index a154e7b..0000000 --- a/B/backpropagation.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: backpropagation -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bag_of_words.md b/B/bag_of_words.md deleted file mode 100644 index fbdc137..0000000 --- a/B/bag_of_words.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bag_of_words -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/baseline.md b/B/baseline.md deleted file mode 100644 index b6d728a..0000000 --- a/B/baseline.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: baseline -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/batch.md b/B/batch.md deleted file mode 100644 index 9cbd7d6..0000000 --- a/B/batch.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: batch -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/batch_normalization.md b/B/batch_normalization.md deleted file mode 100644 index a34b099..0000000 --- a/B/batch_normalization.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: batch_normalization -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/batch_size.md b/B/batch_size.md deleted file mode 100644 index c9d167c..0000000 --- a/B/batch_size.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: batch_size -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bayesian_neural_network.md b/B/bayesian_neural_network.md deleted file mode 100644 index afe0bd5..0000000 --- a/B/bayesian_neural_network.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bayesian_neural_network -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bellman_equation.md b/B/bellman_equation.md deleted file mode 100644 index 3b2e789..0000000 --- a/B/bellman_equation.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bellman_equation -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bias_(ethics-fairness).md b/B/bias_(ethics-fairness).md deleted file mode 100644 index a312697..0000000 --- a/B/bias_(ethics-fairness).md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bias_(ethics-fairness) -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bias_(math).md b/B/bias_(math).md deleted file mode 100644 index ca93bca..0000000 --- a/B/bias_(math).md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bias_(math) -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bigram.md b/B/bigram.md deleted file mode 100644 index 3ca8b92..0000000 --- a/B/bigram.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bigram -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/binary_classification.md b/B/binary_classification.md deleted file mode 100644 index e2de9c8..0000000 --- a/B/binary_classification.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: binary_classification -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/binning.md b/B/binning.md deleted file mode 100644 index aca01c8..0000000 --- a/B/binning.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: binning -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/boosting.md b/B/boosting.md deleted file mode 100644 index 54eca87..0000000 --- a/B/boosting.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: boosting -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bounding_box.md b/B/bounding_box.md deleted file mode 100644 index 9eb38ac..0000000 --- a/B/bounding_box.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bounding_box -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/broadcasting.md b/B/broadcasting.md deleted file mode 100644 index fc82088..0000000 --- a/B/broadcasting.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: broadcasting -tags: - - word - - B ---- - \ No newline at end of file diff --git a/B/bucketing.md b/B/bucketing.md deleted file mode 100644 index a6599c5..0000000 --- a/B/bucketing.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -layout: word -word: bucketing -tags: - - word - - B ---- - \ No newline at end of file diff --git a/_admin/config.yml b/admin/config.yml similarity index 51% rename from _admin/config.yml rename to admin/config.yml index 3a434c8..b94d13d 100644 --- a/_admin/config.yml +++ b/admin/config.yml @@ -10,13 +10,14 @@ media_folder: "_assets/img" # Media files will be stored in the repo under stati public_folder: "/assets/img" # The src attribute for uploaded media will begin with /images/uploads collections: - # Our blog posts - - name: "word" # Used in routes, e.g., /admin/collections/blog - label: "Word" # Used in the UI - folder: "/" # The path to the folder where the documents are stored - create: true # Allow users to create new documents in this collection - slug: "{{word}}" # Filename template, e.g., YYYY-MM-DD-title.md + - 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", @@ -35,6 +36,7 @@ collections: - label: "Tags" name: "tags" widget: "select" + multiple: true options: [ "A", @@ -74,33 +76,81 @@ collections: - { label: "Body", name: "body", widget: "markdown" } view_filters: - - label: "Alice's and Bob's Posts" - field: author - pattern: "Alice|Bob" - - label: "Posts published in 2020" - field: date - pattern: "2020" - - label: Drafts - field: draft - pattern: true - - - name: pages - label: Pages - label_singular: "Page" - folder: words - create: true - # adding a nested object will show the collection folder structure - nested: - depth: 2 # max depth to show in the collection tree - summary: "{{title}}" # optional summary for a tree node, defaults to the inferred title field - identifier_field: word - fields: - - label: Title - name: word - widget: string - - label: Body - name: body - widget: markdown - # adding a meta object with a path property allows editing the path of entries - # moving an existing entry will move the entire sub tree of the entry to the new location - meta: { path: { widget: string, label: "Path", index_file: "index" } } + - 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/_admin/index.html b/admin/index.html similarity index 100% rename from _admin/index.html rename to admin/index.html diff --git a/_assets/index.css b/assets/index.css similarity index 100% rename from _assets/index.css rename to assets/index.css diff --git a/_assets/index.js b/assets/index.js similarity index 100% rename from _assets/index.js rename to assets/index.js diff --git a/_assets/index2.css b/assets/index2.css similarity index 100% rename from _assets/index2.css rename to assets/index2.css diff --git a/_assets/word.css b/assets/word.css similarity index 100% rename from _assets/word.css rename to assets/word.css diff --git a/_assets/words.css b/assets/words.css similarity index 100% rename from _assets/words.css rename to assets/words.css diff --git a/index2.njk b/new_index.njk similarity index 100% rename from index2.njk rename to new_index.njk diff --git a/public/A/a-b_testing/index.html b/public/A/a-b_testing/index.html new file mode 100644 index 0000000..1a6c016 --- /dev/null +++ b/public/A/a-b_testing/index.html @@ -0,0 +1,12 @@ + + +
+ +

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.

+
+ +
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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

+
+ +
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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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+ + 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 + +

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 + +

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 + +

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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+ + backpropagation + + + +
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+ + batch_size + + + +
+ + bayesian_neural_network + + + +
+ + bellman_equation + + + +
+ + bias_(ethics-fairness) + + + +
+ + bias_(math) + + + +
+ + bigram + + + +
+ + binary_classification + + + +
+ + binning + + + +
+ + boosting + + + +
+ + bounding_box + + + +
+ + broadcasting + + + +
+ + bucketing + + + +
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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.

+

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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+ +
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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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+ +
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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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+ + 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 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.

+ + +
+ + checkpoint + +

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.

+ + +
+ + class-imbalanced_dataset + +

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.

+ + +
+ + class + +

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.

+ + +
+ + classification_model + +

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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+ + data_analysis + + + +
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+ + dataframe + + + +
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+ + decision_boundary + + + +
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+ + 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) + +

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) + +

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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+ +
+ +
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+ +
+ +
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+ +
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+ +
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+ +
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+ +
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+ +
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+ +
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+ +
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+ +
+ +
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+ +
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+ +
+ +
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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.

+
+ +
\ No newline at end of file diff --git a/public/T/true_positive_(tp)/index.html b/public/T/true_positive_(tp)/index.html new file mode 100644 index 0000000..cbee067 --- /dev/null +++ b/public/T/true_positive_(tp)/index.html @@ -0,0 +1,11 @@ + + +
+ +

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.

+
+ +
\ No newline at end of file diff --git a/public/T/true_positive_rate_(tpr)/index.html b/public/T/true_positive_rate_(tpr)/index.html new file mode 100644 index 0000000..ac65c2f --- /dev/null +++ b/public/T/true_positive_rate_(tpr)/index.html @@ -0,0 +1,10 @@ + + +
+ +
+ +
\ No newline at end of file diff --git a/public/U/index.html b/public/U/index.html new file mode 100644 index 0000000..b8c3c06 --- /dev/null +++ b/public/U/index.html @@ -0,0 +1,38 @@ +
+ + 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 new file mode 100644 index 0000000..8414200 --- /dev/null +++ b/public/U/unawareness_(to_a_sensitive_attribute)/index.html @@ -0,0 +1,11 @@ + + +
+ +

testing U!

+
+ +
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+ +
+ +
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+ +
+ +
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+ +
+ +
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+ +
+ +
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+ +
+ +
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+ + validation + + + +
+ + validation_set + + + +
+ + vanishing_gradient_problem + + + +
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+ +
+ +
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+ +
+ +
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+ +
+ +
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+ + 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 new file mode 100644 index 0000000..2407bd3 --- /dev/null +++ b/public/X/index.html @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/Y/index.html b/public/Y/index.html new file mode 100644 index 0000000..2407bd3 --- /dev/null +++ b/public/Y/index.html @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/Z/index.html b/public/Z/index.html new file mode 100644 index 0000000..2407bd3 --- /dev/null +++ b/public/Z/index.html @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/public/admin/config.yml b/public/admin/config.yml new file mode 100644 index 0000000..b94d13d --- /dev/null +++ b/public/admin/config.yml @@ -0,0 +1,156 @@ +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 new file mode 100644 index 0000000..302c137 --- /dev/null +++ b/public/admin/index.html @@ -0,0 +1,13 @@ + + + + + + Content Manager + + + + + + + diff --git a/public/assets/index.css b/public/assets/index.css new file mode 100644 index 0000000..98422ae --- /dev/null +++ b/public/assets/index.css @@ -0,0 +1,157 @@ +@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 new file mode 100644 index 0000000..a6d560d --- /dev/null +++ b/public/assets/index.js @@ -0,0 +1,35 @@ +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 new file mode 100644 index 0000000..a0b32fd --- /dev/null +++ b/public/assets/index2.css @@ -0,0 +1,152 @@ +@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 new file mode 100644 index 0000000..8110318 --- /dev/null +++ b/public/assets/word.css @@ -0,0 +1,86 @@ +@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 new file mode 100644 index 0000000..326cdca --- /dev/null +++ b/public/assets/words.css @@ -0,0 +1,66 @@ +@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 new file mode 100644 index 0000000..f899117 --- /dev/null +++ b/public/index.html @@ -0,0 +1,4671 @@ + + + + + + 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.

+ +
+ + 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.

+ +
+ + 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.

+ +
+ + 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.

+ +
+ + 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) + + + + + + + + +
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+ + 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 + + + + + + + + +
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+ + vanishing_gradient_problem + + + + + + + + +
+ W
+ + wasserstein_loss + + + + + + + + +
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+ + weighted_alternating_least_squares_(wals) + + + + + + + + +
+ + wide_model + + + + + + + + +
+ + width + + + + + + + + +
+ X
+ Y
+ Z
+
+ + + + + + \ No newline at end of file diff --git a/C/C.njk b/words/C/C.njk similarity index 100% rename from C/C.njk rename to words/C/C.njk diff --git a/C/calibration_layer.md b/words/C/calibration_layer.md similarity index 100% rename from C/calibration_layer.md rename to words/C/calibration_layer.md diff --git a/C/candidate_generation.md b/words/C/candidate_generation.md similarity index 100% rename from C/candidate_generation.md rename to words/C/candidate_generation.md diff --git a/C/candidate_sampling.md b/words/C/candidate_sampling.md similarity index 100% rename from C/candidate_sampling.md rename to words/C/candidate_sampling.md diff --git a/C/categorical_data.md b/words/C/categorical_data.md similarity index 100% rename from C/categorical_data.md rename to words/C/categorical_data.md diff --git a/C/centroid-based_clustering.md b/words/C/centroid-based_clustering.md similarity index 100% rename from C/centroid-based_clustering.md rename to 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