The Vanishing & Exploding Gradient: Why deep networks almost didn't work

In 2015, Microsoft researchers found their 56-layer model failed to beat its 20-layer counterpart - even on the training set. The culprit: a two-headed monster born from the chain rule - the Vanishing & Exploding Gradient problem. In this video, we break down: 00:00 - The paradox of depth (Microsoft's 2015 experiment) 01:12 - Where it came from: Hochreiter (1991) and Bengio (1994) 02:25 - Intuition: the "line of whispers" analogy for backpropagation 04:03 - The math: why gradients compound like interest (r^L) 05:53 - From toy model to real networks: what "r" actually is 07:27 - 4 real failure modes: vanishing, exploding, saturation, and the knife's edge at r=1 09:20 - The fixes: ReLU, Xavier/He initialization, Batch Normalization, gradient clipping, and the game-changer - Residual (Skip) Connections in ResNet 12:36 - How RNNs, CNNs, and Transformers each solve this differently 14:50 - The limits: what skip connections don't fix, and why the problem resurfaces in very deep Transformers 16:29 - Closing thought: 0.9^100 ≈ 0.0000266 - why a smoothly training deep network is an engineering miracle If you've ever wondered why architectures like ResNet, LSTMs, and Transformers are built the way they are, this video explains the single mathematical problem that shaped all of them. Watch list:    • Gradient descent, Mini-batches & The noise...   Algorithms, computer science, and AI - explained visually, without the fluff. New videos every week. A null pointer points to nothing. This channel points to everything worth understanding about how code actually works. Nullpoint breaks down algorithms, data structures, computer science, and AI into clear, visual explanations you'll actually remember. From sorting and Big-O to neural networks and the ideas behind modern AI - we skip the buzzwords and get to how things really work under the hood. #DeepLearning #NeuralNetworks #MachineLearning #Backpropagation #nullpoint