Neural Networks & Deep Learning

1. 1. From Logistic Regression to Neurons
2. 2. The Perceptron & the XOR Problem
3. 3. Multi-Layer Perceptrons & Why Depth Solves XOR
4. 4. Activation Functions
5. 5. Forward Propagation, Loss Functions & Backpropagation
6. 6. Training in Practice: Regularization for Neural Networks
7. 7. Convolutional Neural Networks (CNNs)
8. 8. Recurrent Neural Networks (RNNs) & LSTMs
9. 9. A Transformer Preview
10. 10. A Framework Tour: PyTorch vs. TensorFlow
11. 11. Capstone: Building and Training a Real Neural Network