Deep Learning
Shallow networks learn simple patterns; everything impressive about modern AI came from going deep, and depth broke training before it worked. This protocol covers the problem that nearly killed deep networks (vanishing gradients), the fixes that revived them, and the two great specializations: convolutional networks that see, recurrent networks that remember.
It closes with the generative family, models that create instead of classify: GANs, VAEs, and the diffusion models behind every image generator you have tried. By the end, the road to transformers is fully paved.
What you'll be able to do
- Explain why deep networks refused to train for years and what fixed them: residual connections, batch norm, better activations
- Follow an image through a CNN, from raw pixels through convolutions, feature maps and pooling to a prediction
- Understand how RNNs and LSTMs carry memory across a sequence, and exactly where they hit their wall
- Tell GANs, VAEs and diffusion models apart and pick the right one for a job
- Reuse pretrained networks with transfer learning instead of training from scratch
Deep Networks
Why go deep? Vanishing gradients, skip connections, batch norm.
0/5 lessons completed · ~39 min
Convolutional Networks
How AI sees images: convolutions, feature maps, transfer learning.
0/5 lessons completed · ~39 min
Recurrent Networks
Sequences and memory: RNN, LSTM, and the road to attention.
0/5 lessons completed · ~37 min