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| <!doctype html> <html lang=en > <meta charset=UTF-8 > <meta name=viewport content="width=device-width, initial-scale=1"> <link rel=stylesheet href="/website/css/franklin.css"> <link rel=stylesheet href="/website/css/poole_hyde.css"> <link rel=stylesheet href="/website/css/custom.css"> <style> html {font-size: 17px;} .franklin-content {position: relative; padding-left: 8%; padding-right: 5%; line-height: 1.35em;} @media (min-width: 940px) { .franklin-content {width: 100%; margin-left: auto; margin-right: auto;} } @media (max-width: 768px) { .franklin-content {padding-left: 6%; padding-right: 6%;} } </style> <link rel=icon href="/website/assets/favicon.png"> <title>Dataflowr - Deep Learning DIY</title> <div class=sidebar > <div class="container sidebar-sticky"> <div class=sidebar-about > <img src="/website/assets/dataflowr_violet_plain_square.png" style="width: 120px; height: auto; display: inline"> <img src="/website/assets/favicon.png" style="margin-left:1em; position:relative;left:0px; top:-30px; width: 60px; height: auto; display: inline"> <h1 style="font-size:1em; opacity: 0.95;"><a href="/website/">Deep Learning DIY</a></h1> </div> <nav class=sidebar-nav > <a class="sidebar-nav-item " href="/website/modules/0-sotfware-installation"> <b>Module 0</b> - <em> Software installation</em> </a> <a class="sidebar-nav-item " href="/website/modules/1-intro-general-overview"> <b>Module 1</b> - <em>Introduction & General Overview</em> </a> <a class="sidebar-nav-item " href="/website/modules/2a-pytorch-tensors"> <b>Module 2a</b> - <em>PyTorch tensors</em> </a> <a class="sidebar-nav-item " href="/website/modules/2b-automatic-differentiation"> <b>Module 2b</b> - <em>Automatic differentiation</em> </a> <a class="sidebar-nav-item " href="/website/modules/2c-jax"> <b>Module 2c</b> - <em>Automatic differentiation: VJP and intro to JAX</em> </a> <a class="sidebar-nav-item " href="/website/modules/3-loss-functions-for-classification"> <b>Module 3</b> - <em>Loss functions for classification</em> </a> <a class="sidebar-nav-item " href="/website/modules/4-optimization-for-deep-learning"> <b>Module 4</b> - <em>Optimization for DL</em> </a> <a class="sidebar-nav-item " href="/website/modules/5-stacking-layers"> <b>Module 5</b> - <em>Stacking layers</em> </a> <a class="sidebar-nav-item " href="/website/modules/6-convolutional-neural-network"> <b>Module 6</b> - <em>Convolutional neural network</em> </a> <a class="sidebar-nav-item " href="/website/modules/7-dataloading"> <b>Module 7</b> - <em>Dataloading</em> </a> <a class="sidebar-nav-item " href="/website/modules/8a-embedding-layers"> <b>Module 8a</b> - <em>Embedding layers</em> </a> <a class="sidebar-nav-item " href="/website/modules/8b-collaborative-filtering"> <b>Module 8b</b> - <em>Collaborative filtering</em> </a> <a class="sidebar-nav-item " href="/website/modules/8c-word2vec"> <b>Module 8c</b> - <em>Word2vec</em> </a> <a class="sidebar-nav-item " href="/website/modules/9a-autoencoders"> <b>Module 9a</b> - <em>Autoencoders</em> </a> <a class="sidebar-nav-item " href="/website/modules/9b-unet"> <b>Module 9b</b> - <em>UNets</em> </a> <a class="sidebar-nav-item " href="/website/modules/9c-flows"> <b>Module 9c</b> - <em>Flows</em> </a> <a class="sidebar-nav-item " href="/website/modules/10-generative-adversarial-networks"> <b>Module 10</b> - <em>Generative adversarial networks</em> </a> <a class="sidebar-nav-item " href="/website/modules/11a-recurrent-neural-networks-theory"> <b>Module 11a</b> - <em>Recurrent Neural Networks (theory)</em> </a> <a class="sidebar-nav-item " href="/website/modules/11b-recurrent-neural-networks-practice"> <b>Module 11b</b> - <em>RNN in practice</em> </a> <a class="sidebar-nav-item " href="/website/modules/11c-batches-with-sequences"> <b>Module 11c</b> - <em>Batches with sequences in Pytorch</em> </a> <a class="sidebar-nav-item " href="/website/modules/12-attention"> <b>Module 12</b> - <em>Attention and Transformers</em> </a> <a class="sidebar-nav-item " href="/website/modules/13-siamese"> <b>Module 13</b> - <em>Siamese Networks and Representation Learning</em> </a> <a class="sidebar-nav-item " href="/website/modules/14a-depth"> <b>Module 14a</b> - <em>The Benefits of Depth</em> </a> <a class="sidebar-nav-item " href="/website/modules/14b-depth"> <b>Module 14b</b> - <em>The Problems with Depth</em> </a> <a class="sidebar-nav-item " href="/website/modules/15-dropout"> <b>Module 15</b> - <em>Dropout</em> </a> <a class="sidebar-nav-item " href="/website/modules/16-batchnorm"> <b>Module 16</b> - <em>Batchnorm</em> </a> <a class="sidebar-nav-item " href="/website/modules/17-resnets"> <b>Module 17</b> - <em>Resnets</em> </a> <a class="sidebar-nav-item " href="/website/modules/18a-diffusion"> <b>Module 18a</b> - <em>Denoising Diffusion Probabilistic Models</em> </a> <!-- <div class=week >Unit 7</div>--> <div class=week >Homeworks</div> <a class="sidebar-nav-item " href="/website/homework/1-mlp-from-scratch"> <b>Homework 1</b> - <em>MLP from scratch</em> </a> <a class="sidebar-nav-item " href="/website/homework/2-CAM-adversarial"> <b>Homework 2</b> - <em>Class Activation Map and adversarial examples</em> </a> <a class="sidebar-nav-item active" href="/website/homework/3-VAE"> <b>Homework 3</b> - <em>VAE for MNIST clustering and generation</em> </a> <div class=week >Bonus</div> <a class="sidebar-nav-item " href="/website/modules/12-intro-julia"> <b>Module</b> - <em>Intro to Julia: Autodiff with dual numbers</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph0"> <b>Module</b> - <em>Deep learning on graphs</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph1"> <b>Graph</b> - <em>Node embeddings</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph2"> <b>Graph</b> - <em>Signal processing on graphs</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph3"> <b>Graph</b> - <em> Graph embeddings and GNNs</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/GCN_inductivebias_spectral"> <b>Post</b> - <em>Spectral GCN</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/Convolutions_first"> <b>Post</b> - <em>Convolutions from first principles</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/invariant_equivariant"> <b>Post</b> - <em>Invariant and equivariant networks</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/graph_invariant"> <b>Graph</b> - <em>Exploiting Graph Invariants in Deep Learning</em> </a> <div class=week >Guest Lectures</div> <a class="sidebar-nav-item " href="/website/modules/privacy-preserving-ML"> <b>Privacy Preserving ML</b> - <em>Daniel Huynh</em> </a> </nav> </div> </div> <div class="content container"> <div class=franklin-content ><h1 id=homework_3_vae_for_mnist_clustering_and_generation ><a href="#homework_3_vae_for_mnist_clustering_and_generation" class=header-anchor >Homework 3: VAE for MNIST clustering and generation</a></h1> <img src="/website/assets/mnist_disentangled.gif" style="width: 620px; height: auto; display: inline"> <p><a href="https://github.com/Schlumberger/joint-vae">Image source</a></p> <p>Homework 3 is in the form of a jupyter notebook. You must complete it and submit it on moodle (for students enrolled on this course).</p> <p><a href="https://github.com/dataflowr/notebooks/blob/master/HW3/VAE_clustering_empty.ipynb">The Jupyter notebook</a></p> <div class=page-foot > <div class=copyright > <a href="https://github.com/dataflowr/website/tree/master"><b>Edit this page on <img class=github-logo src="https://unpkg.com/[email protected]/dist/svg/logo-github.svg"></b></a> Last modified: June 20, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. </div> </div> </div> </div> | ||
| <!doctype html> <html lang=en > <meta charset=UTF-8 > <meta name=viewport content="width=device-width, initial-scale=1"> <link rel=stylesheet href="/website/css/franklin.css"> <link rel=stylesheet href="/website/css/poole_hyde.css"> <link rel=stylesheet href="/website/css/custom.css"> <style> html {font-size: 17px;} .franklin-content {position: relative; padding-left: 8%; padding-right: 5%; line-height: 1.35em;} @media (min-width: 940px) { .franklin-content {width: 100%; margin-left: auto; margin-right: auto;} } @media (max-width: 768px) { .franklin-content {padding-left: 6%; padding-right: 6%;} } </style> <link rel=icon href="/website/assets/favicon.png"> <title>Dataflowr - Deep Learning DIY</title> <div class=sidebar > <div class="container sidebar-sticky"> <div class=sidebar-about > <img src="/website/assets/dataflowr_violet_plain_square.png" style="width: 120px; height: auto; display: inline"> <img src="/website/assets/favicon.png" style="margin-left:1em; position:relative;left:0px; top:-30px; width: 60px; height: auto; display: inline"> <h1 style="font-size:1em; opacity: 0.95;"><a href="/website/">Deep Learning DIY</a></h1> </div> <nav class=sidebar-nav > <a class="sidebar-nav-item " href="/website/modules/0-sotfware-installation"> <b>Module 0</b> - <em> Software installation</em> </a> <a class="sidebar-nav-item " href="/website/modules/1-intro-general-overview"> <b>Module 1</b> - <em>Introduction & General Overview</em> </a> <a class="sidebar-nav-item " href="/website/modules/2a-pytorch-tensors"> <b>Module 2a</b> - <em>PyTorch tensors</em> </a> <a class="sidebar-nav-item " href="/website/modules/2b-automatic-differentiation"> <b>Module 2b</b> - <em>Automatic differentiation</em> </a> <a class="sidebar-nav-item " href="/website/modules/2c-jax"> <b>Module 2c</b> - <em>Automatic differentiation: VJP and intro to JAX</em> </a> <a class="sidebar-nav-item " href="/website/modules/3-loss-functions-for-classification"> <b>Module 3</b> - <em>Loss functions for classification</em> </a> <a class="sidebar-nav-item " href="/website/modules/4-optimization-for-deep-learning"> <b>Module 4</b> - <em>Optimization for DL</em> </a> <a class="sidebar-nav-item " href="/website/modules/5-stacking-layers"> <b>Module 5</b> - <em>Stacking layers</em> </a> <a class="sidebar-nav-item " href="/website/modules/6-convolutional-neural-network"> <b>Module 6</b> - <em>Convolutional neural network</em> </a> <a class="sidebar-nav-item " href="/website/modules/7-dataloading"> <b>Module 7</b> - <em>Dataloading</em> </a> <a class="sidebar-nav-item " href="/website/modules/8a-embedding-layers"> <b>Module 8a</b> - <em>Embedding layers</em> </a> <a class="sidebar-nav-item " href="/website/modules/8b-collaborative-filtering"> <b>Module 8b</b> - <em>Collaborative filtering</em> </a> <a class="sidebar-nav-item " href="/website/modules/8c-word2vec"> <b>Module 8c</b> - <em>Word2vec</em> </a> <a class="sidebar-nav-item " href="/website/modules/9a-autoencoders"> <b>Module 9a</b> - <em>Autoencoders</em> </a> <a class="sidebar-nav-item " href="/website/modules/9b-unet"> <b>Module 9b</b> - <em>UNets</em> </a> <a class="sidebar-nav-item " href="/website/modules/9c-flows"> <b>Module 9c</b> - <em>Flows</em> </a> <a class="sidebar-nav-item " href="/website/modules/10-generative-adversarial-networks"> <b>Module 10</b> - <em>Generative adversarial networks</em> </a> <a class="sidebar-nav-item " href="/website/modules/11a-recurrent-neural-networks-theory"> <b>Module 11a</b> - <em>Recurrent Neural Networks (theory)</em> </a> <a class="sidebar-nav-item " href="/website/modules/11b-recurrent-neural-networks-practice"> <b>Module 11b</b> - <em>RNN in practice</em> </a> <a class="sidebar-nav-item " href="/website/modules/11c-batches-with-sequences"> <b>Module 11c</b> - <em>Batches with sequences in Pytorch</em> </a> <a class="sidebar-nav-item " href="/website/modules/12-attention"> <b>Module 12</b> - <em>Attention and Transformers</em> </a> <a class="sidebar-nav-item " href="/website/modules/13-siamese"> <b>Module 13</b> - <em>Siamese Networks and Representation Learning</em> </a> <a class="sidebar-nav-item " href="/website/modules/14a-depth"> <b>Module 14a</b> - <em>The Benefits of Depth</em> </a> <a class="sidebar-nav-item " href="/website/modules/14b-depth"> <b>Module 14b</b> - <em>The Problems with Depth</em> </a> <a class="sidebar-nav-item " href="/website/modules/15-dropout"> <b>Module 15</b> - <em>Dropout</em> </a> <a class="sidebar-nav-item " href="/website/modules/16-batchnorm"> <b>Module 16</b> - <em>Batchnorm</em> </a> <a class="sidebar-nav-item " href="/website/modules/17-resnets"> <b>Module 17</b> - <em>Resnets</em> </a> <a class="sidebar-nav-item " href="/website/modules/18a-diffusion"> <b>Module 18a</b> - <em>Denoising Diffusion Probabilistic Models</em> </a> <!-- <div class=week >Unit 7</div>--> <div class=week >Homeworks</div> <a class="sidebar-nav-item " href="/website/homework/1-mlp-from-scratch"> <b>Homework 1</b> - <em>MLP from scratch</em> </a> <a class="sidebar-nav-item " href="/website/homework/2-CAM-adversarial"> <b>Homework 2</b> - <em>Class Activation Map and adversarial examples</em> </a> <a class="sidebar-nav-item active" href="/website/homework/3-VAE"> <b>Homework 3</b> - <em>VAE for MNIST clustering and generation</em> </a> <div class=week >Bonus</div> <a class="sidebar-nav-item " href="/website/modules/12-intro-julia"> <b>Module</b> - <em>Intro to Julia: Autodiff with dual numbers</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph0"> <b>Module</b> - <em>Deep learning on graphs</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph1"> <b>Graph</b> - <em>Node embeddings</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph2"> <b>Graph</b> - <em>Signal processing on graphs</em> </a> <a class="sidebar-nav-item " href="/website/modules/graph3"> <b>Graph</b> - <em> Graph embeddings and GNNs</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/GCN_inductivebias_spectral"> <b>Post</b> - <em>Spectral GCN</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/Convolutions_first"> <b>Post</b> - <em>Convolutions from first principles</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/invariant_equivariant"> <b>Post</b> - <em>Invariant and equivariant networks</em> </a> <a class="sidebar-nav-item " href="/website/modules/extras/graph_invariant"> <b>Graph</b> - <em>Exploiting Graph Invariants in Deep Learning</em> </a> <div class=week >Guest Lectures</div> <a class="sidebar-nav-item " href="/website/modules/privacy-preserving-ML"> <b>Privacy Preserving ML</b> - <em>Daniel Huynh</em> </a> </nav> </div> </div> <div class="content container"> <div class=franklin-content ><h1 id=homework_3_vae_for_mnist_clustering_and_generation ><a href="#homework_3_vae_for_mnist_clustering_and_generation" class=header-anchor >Homework 3: VAE for MNIST clustering and generation</a></h1> <img src="/website/assets/mnist_disentangled.gif" style="width: 620px; height: auto; display: inline"> <p><a href="https://github.com/Schlumberger/joint-vae">Image source</a></p> <p>Homework 3 is in the form of a jupyter notebook. You must complete it and submit it on moodle (for students enrolled on this course).</p> <p><a href="https://github.com/dataflowr/notebooks/blob/master/HW3/VAE_clustering_empty.ipynb">The Jupyter notebook</a></p> <div class=page-foot > <div class=copyright > <a href="https://github.com/dataflowr/website/tree/master"><b>Edit this page on <img class=github-logo src="https://unpkg.com/[email protected]/dist/svg/logo-github.svg"></b></a> Last modified: September 15, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. </div> </div> </div> </div> |
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| <a href="https://github.com/dataflowr/website/tree/master"><b>Edit this page on <img class=github-logo src="https://unpkg.com/[email protected]/dist/svg/logo-github.svg"></b></a> | ||
| Last modified: June 20, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. | ||
| Last modified: September 15, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. | ||
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| <a href="https://github.com/dataflowr/website/tree/master"><b>Edit this page on <img class=github-logo src="https://unpkg.com/[email protected]/dist/svg/logo-github.svg"></b></a> | ||
| Last modified: June 20, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. | ||
| Last modified: September 15, 2023. Website built with <a href="https://github.com/tlienart/Franklin.jl">Franklin.jl</a> and the <a href="https://julialang.org">Julia programming language</a>. | ||
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