Understanding Gradient Descent for Over-parameterized Deep Neural Networks Published 2020-07-03 Download video MP4 360p Download video MP4 720p Recommendations 15:17 The replica symmetric phase of random constraint satisfaction problems 20:33 Gradient descent, how neural networks learn | Chapter 2, Deep learning 16:37 Recurrent Neural Networks (RNNs), Clearly Explained!!! 36:27 Part 8 - Elliott Lieb: "Understanding entropy without probability" 46:29 Stat-mech NN 12:47 What is backpropagation really doing? | Chapter 3, Deep learning 20:45 Long Short-Term Memory (LSTM), Clearly Explained 12:35 Google Search as We Know It is Gone! 24:53 Tajemniczy ciąg Fibonacciego. Złota liczba. Boska proporcja 14:58 Support Vector Machines: All you need to know! 08:29 Google Data Center 360° Tour 58:46 Ways to think about the brain: Emergence of cognition from action | ISTA Lecture with Gyorgy Buzsaki 17:34 Neural Networks Pt. 2: Backpropagation Main Ideas 32:40 Decision Tree In Machine Learning | Decision Tree Algorithm In Python |Machine Learning |Simplilearn 16:12 Word Embedding and Word2Vec, Clearly Explained!!! 59:52 MIT 6.S191 (2023): Deep Generative Modeling 22:31 threading vs multiprocessing in python 11:55 "Poetyka" Arystotelesa . Tragedia antyczna w świetle poglądów Arystotelesa. 15:28 What are Diffusion Models? 16:30 Why do Convolutional Neural Networks work so well? Similar videos 15:39 Gradient descent simple explanation|gradient descent machine learning|gradient descent algorithm 04:19 Double Descent explained by Yann LeCun 09:59 Deep Double Descent 53:34 Holger Rauhut - The implicit bias of gradient descent for learning deep neural networks 57:33 ML Seminars - On the convergence of gradient descent for wide two-layer neural networks 52:12 Mikhail Belkin: "Optimization for over-parameterized systems of non-linear equations" 44:56 Francis Bach: Gradient descent on infinitely wide neural networks: Global convergence and... 17:33 The mystery of over-parametrization in neural networks Behnam Neyshabur 44:20 Analyzing Optimization and Generalization in Deep Learning via Dynamics of Gradient Descent 1:00:52 On the Global Convergence of Gradient Descent for (...) - Bach - Workshop 3 - CEB T1 2019 59:46 Insights on Gradient-Based Algorithms in High-Dimensional Learning 1:05:07 Nadav Cohen - Analyzing Optimization and Generalization in DL via Dynamics of Gradient Descent 54:10 Guido Montúfar: Implicit bias of gradient descent for MSE regression with wide neural networks 31:21 Asaf Noy - A Convergence Theory Towards Practical Over parameterized Deep Neural Networks 21:00 Surya Ganguli - A mathematical theory of semantic development in deep neural networks 40:08 The Most Important Algorithm in Machine Learning 44:07 Quanquan Gu: "Learning Over-parameterized Neural Networks: From Neural Tangent Kernel to Mean-fi..." 51:50 Stochastic Gradient Descent: where optimization meets machine learning- Rachel Ward 04:03 NeurIPS 2019:Generalization Bounds of stochastic Gradient Descent for Wide and Deep Neural Networks More results