Hands-On Evolution of Deep Learning – Geoffrey Hinton’s AI Legacy

<p>Geoffrey Hinton is known as the "the Godfather of AI".</p> <p>We just published a course on the <a href="http://freeCodeCamp.org">freeCodeCamp.org</a> YouTube channel that will help you master modern neural networks by recreating the groundbreaking discoveries of Geoffrey Hinton. The course covers his key papers and you will follow the evolution of the ideas across decades, from Boltzmann Machines and backpropagation to Deep Belief Networks, t-SNE, AlexNet, Dropout, Knowledge Distillation, Capsule Networks, Layer Normalization, and the Forward-Forward Algorithm.</p> <p>Throughout this 27-hour course, Mohammed Abrah will explain the motivation behind each work, the problem it addressed, the core idea introduced by the paper, and then connect the theory to an educational implementation in PyTorch.</p> <p>The tutorial explores:</p> <ul> <li><p>The historical evolution of Geoffrey Hinton's research across deep learning</p> </li> <li><p>How Boltzmann Machines introduced learning through energy-based models</p> </li> <li><p>Learning representations through backpropagation</p> </li> <li><p>Distributed representations and Adaptive Mixtures of Local Experts</p> </li> <li><p>The Helmholtz Machine and the Wake-Sleep Algorithm</p> </li> <li><p>Stochastic Neighbor Embedding and the foundations of t-SNE</p> </li> <li><p>Deep Belief Networks and greedy layer-wise pretraining</p> </li> <li><p>Deep Boltzmann Machines</p> </li> <li><p>How Rectified Linear Units helped improve Restricted Boltzmann Machines</p> </li> <li><p>ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)</p> </li> <li><p>Dropout as a method for reducing overfitting</p> </li> <li><p>Knowledge Distillation and transferring knowledge between neural networks</p> </li> <li><p>Layer Normalization</p> </li> <li><p>Dynamic Routing Between Capsules</p> </li> <li><p>Contrastive Learning of Visual Representations</p> </li> <li><p>The Forward-Forward Algorithm</p> </li> <li><p>Practical PyTorch implementations of the core ideas</p> </li> <li><p>Visualizations and experiments that make the underlying algorithms easier to understand</p> </li> <li><p>The connection between these historical ideas and modern AI architectures</p> </li> </ul> <p>Watch the full course on <a href="https://youtu.be/BMYvfVk8Ar0">the freeCodeCamp.org YouTube channel</a> (27-hour watch).</p> <div class="embed-wrapper"><iframe width="560" height="315" src="https://www.youtube.com/embed/BMYvfVk8Ar0" style="aspect-ratio: 16 / 9; width: 100%; height: auto;" title="YouTube video player" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen="" loading="lazy"></iframe></div>