Neural Networks Demystified [Part 7: Overfitting, Testing, and Regularization] Published -- Download video MP4 360p Recommendations 24:07 AI can't cross this line and we don't know why. 18:08 Transformer Neural Networks Derived from Scratch 08:00 What is Back Propagation 11:40 Regularization in a Neural Network | Dealing with overfitting 17:38 The moment we stopped understanding AI [AlexNet] 25:28 Watching Neural Networks Learn 57:24 Terence Tao at IMO 2024: AI and Mathematics 31:28 Building a neural network FROM SCRATCH (no Tensorflow/Pytorch, just numpy & math) 37:05 Brain Criticality - Optimizing Neural Computations 10:30 Why Neural Networks can learn (almost) anything 20:33 Gradient descent, how neural networks learn | Chapter 2, Deep learning 12:56 Convolutional Neural Networks from Scratch | In Depth 2:25:52 The spelled-out intro to neural networks and backpropagation: building micrograd 25:28 Dendrites: Why Biological Neurons Are Deep Neural Networks 22:08 Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation Similar videos 06:00 Neural Networks Demystified: Part 7, Welch Labs @ MLconf SF 06:56 Neural Networks Demystified [Part 3: Gradient Descent] 04:34 Overfitting and Regularization For Deep Learning | Two Minute Papers #56 07:56 Neural Networks Demystified [Part 4: Backpropagation] 04:16 Overfitting in a Neural Network explained 00:17 Regularization 22:45 9.1: L1 and L2 Regularization with Keras and TensorFlow (Module 9, Part 1) 07:10 Why Regularization Reduces Overfitting (C2W1L05) 08:29 9.2: Using L1 and L2 Regularization in Keras and TensorFlow (Module 9, Part 2) More results