Geoffrey Hinton: "Introduction to Deep Learning & Deep Belief Nets" Published 2015-08-24 Download video MP4 360p Download video MP4 720p Recommendations 1:01:34 Geoffrey Hinton: "Using Backpropagation for Fine-Tuning a Generative Model..." 58:12 MIT Introduction to Deep Learning (2023) | 6.S191 28:22 Geoffrey Hinton: The Foundations of Deep Learning 1:04:48 Stéphane Mallat: "Scattering Invariant Deep Networks for Classification, Pt. 1" 1:01:50 Iain Murray: "Introduction to MCMC for Deep Learning" 39:46 Heroes of Deep Learning: Andrew Ng interviews Geoffrey Hinton 1:17:38 Steve Brunton: "Dynamical Systems (Part 1/2)" 51:31 11. Introduction to Machine Learning 23:51 Deep Learning Theory Session. Ilya SutskeverIlya Sutskever 1:08:06 Deep Learning Basics: Introduction and Overview 18:40 But what is a neural network? | Chapter 1, Deep learning 58:02 Steve Brunton - Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics 1:11:36 Geoffrey Hinton talk "What is wrong with convolutional neural nets ?" 59:24 The Next Generation of Neural Networks 1:12:03 Steve Brunton: "Introduction to Fluid Mechanics" 1:25:13 Stanford Seminar - Can the brain do back-propagation? Geoffrey Hinton 50:26 Tom Goldstein: "What do neural loss surfaces look like?" 36:34 Geoffrey Hinton: Large Language Models in Medicine. They Understand and Have Empathy 48:52 MIT 6.S191: Evidential Deep Learning and Uncertainty 47:48 Visualizing and Understanding Deep Neural Networks by Matt Zeiler Similar videos 12:36 Lecture 13.2 — Belief Nets — [ Deep Learning | Geoffrey Hinton | UofT ] 04:32 Deep Belief Nets - Ep. 7 (Deep Learning SIMPLIFIED) 11:26 Lecture 13.3 — Learning sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ] 08:31 Lecture 1.2 — What are neural networks — [ Deep Learning | Geoffrey Hinton | UofT ] 11:26 Lecture 13.3 — Learning sigmoid belief nets [Neural Networks for Machine Learning] 32:28 Geoffrey Hinton: Turing Award Lecture "The Deep Learning Revolution" 17:24 Lecture 7.1 — Modeling sequences a brief overview — [ Deep Learning | Geoffrey Hinton | UofT ] 58:12 Lecture 14/16 : Deep neural nets with generative pre-training 17:12 Lecture 14.5 — RBMs are infinite sigmoid belief nets — [ Deep Learning | Geoffrey Hinton | UofT ] 13:22 Neural networks [7.7] : Deep learning - deep belief network 07:20 3 Deep Belief Networks 07:15 Lecture 12.4 — An example of RBM learning — [ Deep Learning | Geoffrey Hinton | UofT ] 09:41 Lecture 14.2 — Discriminative learning for DBNs — [ Deep Learning | Geoffrey Hinton | UofT ] 03:30 Deep Belief Network (DBN) and autoencoders 07:19 Geoffrey Hinton: The Godfather of Deep Learning 12:36 Lecture 13.2 — Belief Nets [Neural Networks for Machine Learning] 17:35 Lecture 14.1 — Learning layers of features by stacking RBMs — [ Deep Learning | Hinton | UofT ] 05:10 Lecture 2.4 — Why the learning works — [ Deep Learning | Geoffrey Hinton | UofT ] More results