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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
Skip to content
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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
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Repository files navigation

Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content
BranchesTags

Repository files navigation

Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

About

Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

Resources

Readme
Activity

Stars

2 stars

Watchers

2 watching

Forks

1 fork
Report repository

Releases

Packages

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Guide to Deep Learning for Teens in 6 Sessions

This course was prepared for the Nanohacker community. Thanks to Thoughtworks for hosting our sessions!

$ git clone https://github.com/deepdeepdot/nano-deep-learning.git
$ cd nano-deep-learning

If you have python 2.7

$ python -m SimpleHTTPServer

If you have python 3

$ python -m http.server

Overview

It requires proficiency in a programming language, say Ruby or Javascript. Also, it assumes knowledge of basic git commands and github. We'll cover some computer science topics like trees, functional programming using Python, and classes in Python.

Prerequisites

  • Experience with programming
  • Full stack web development

Math Cheatsheet https://www.flickr.com/photos/95869671@N08/40544016221Match Cheatsheet Image

  • Math concepts: matrix, functions, basic stats
  • Programming with Python: images, plotting functions
  • Intro to deep learning concepts
  • Neural Networks: NN, CNN, RNN
  • Overview of popular APIs
    • tensorflow, keras, pytorch
  • Deep Learning with Javascript
    • ml5.js, magenta.js, tensorflow.js

Sources

I decided not to recreate slides that have been wonderfully created by other free online course and also reuse much of the available online material on the net.

Tutorials

Slides

Sections (tentative)

Session #1: Matrix and Convolutions

  • Math: Matrix
  • Image filters
  • Python installation, running

Session #2: Functions and Plotting

  • Jupyter notebooks and ipython
  • Python: lambdas, map, numpy
  • Plotting a function using matplotlib

Session #3: Music Generation

  • Irish music generation using Deep Learning
  • Scrapping ABC Music files
  • Math: Slope of a function

Session #4: Image Classifiers

  • Normal distribution
  • Image classifiers: MNIST, CIFAR, ml5.js
  • ml5.js

Session #5: Text and Language

  • Binary tree and tree recursion
  • Tensorflow as a computational graph
  • Andrej Karpathy's RNN
  • Magenta.js: Drum RNN
  • Rasa, chatbot AI

Session #6: Neural Networks

  • Tensorflow.js, api and demos
  • Big picture of Deep Learning
  • Deep Learning Problems
  • Model Zoo
  • Neural Networks

Slides from IntroToDeepLearning.com

  1. NN, Neural Networks
  2. CNN, Convolutional NN, Computer Vision
  3. RNN, Recurrent NN, Sequence Modeling

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Guide to Deep Learning for Teens in 6 sessions (basic intro to math: matrix, functions, stats)

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