Stanford Seminar - Information Theory of Deep Learning, Naftali Tishby Published 2018-04-05 Download video MP4 360p Download video MP4 720p Recommendations 18:40 But what is a neural network? | Chapter 1, Deep learning 58:12 MIT Introduction to Deep Learning | 6.S191 29:11 Huffman Codes: An Information Theory Perspective 1:15:46 Deep Learning Bootcamp: Kaiming He 1:01:45 Information, Evolution, and intelligent Design - With Daniel Dennett 1:17:29 Stanford CS25: V4 I Overview of Transformers 33:03 Why Some Designs Are Impossible to Improve: Quintessence 12:33 Why Information Theory is Important - Computerphile 1:42:18 Intro to Machine Learning & Neural Networks. How Do They Work? 06:34 When Computers Write Proofs, What's the Point of Mathematicians? 27:07 Attention Is All You Need 09:09 Neural Network Architectures & Deep Learning 16:35 Entropy (for data science) Clearly Explained!!! 09:40 Consciousness Theory Declared "Pseudoscience" by 124 Researchers: IIT's Adversarial Collaboration 1:00:15 Ilya Sutskever: OpenAI Meta-Learning and Self-Play | MIT Artificial General Intelligence (AGI) 58:46 Harnessing The Power Of Information | Order and Disorder | Spark 38:27 ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein 1:11:41 Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy 31:22 The Trillion Dollar Equation Similar videos 1:47:05 001. Information Theory of Deep Learning - Naftali Tishby 57:55 18. Information Theory of Deep Learning. Naftali Tishby 1:48:47 Запись трансляции "Information Theory of Deep Learning" (проф.Naftali Tishby) 08:18 Naftali Tishby | Information Theory of Deep Learning 1:22:23 Machine Learning 2 - Features, Neural Networks | Stanford CS221: AI (Autumn 2019) 1:09:06 Estimating the Information Flow in Deep Neural Networks 1:21:57 Information Theory meets Machine Learning - Information Theory Lecture 16 1:17:04 Stanford Seminar - Recent Advances in Deep Learning 1:45:17 Prof. Naftali Tishby - Towards Interpretable Deep Neural Nets 1:18:17 Stanford CS230: Deep Learning | Autumn 2018 | Lecture 3 - Full-Cycle Deep Learning Projects 1:10:17 The Information Bottleneck Theory of [simple] Deep Learning - Naftali Tishby 1:00:09 Naftali Tishby - The Information Bottleneck View of Deep Learning: Why do we need it? 1:06:17 Stanford Seminar - NVIDIA GPU Computing: A Journey from PC Gaming to Deep Learning 1:00:12 Michael Mahoney: "Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks" 1:19:21 Complete Statistical Theory of Learning (Vladimir Vapnik) | MIT Deep Learning Series 20:26 Recent Advances in the Information Theory of Deep Neural Networks and the Computational Benefits... 1:49:15 Information Theory of Deep Learning - What Do the Layers of Deep Neural Networks Represent? 1:15:45 Stanford Seminar - Towards theories of single-trial high dimensional neural data analysis 00:58 Why Do Tree Based-Models Outperform Neural Nets on Tabular Data? More results