Neural Networks And 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