CNN, ResNet, RNN, LSTM and the Birth of Attention: How Neural Networks Learned to See and Remember

Plain neural networks can’t handle images or sequences. This episode shows the two families of ideas that fixed that: networks that see, and networks that remember. You’ll learn: • Why a plain network struggles with images and with text • Convolutional neural networks: shared filters, pooling and classification • Why very deep networks stopped training, and how ResNet’s skip connections fixed it • Recurrent networks and hidden state • LSTM: the cell state and the forget, input and output gates • Sequence-to-sequence models and the one-vector bottleneck • The first attention mechanism • Autoencoders and GANs: how networks learned to create Each step is a limit, then the fix. Part 3 shows what happens when attention replaces everything else. #AI #CNN #RNN #LSTM #DeepLearning #Attention