Neural Networks from Scratch in Python

NNFS Chapter 1 - Introducing Neural Networks
NNFS Chapter 2 - Coding Our First Neurons
NNFS Chapter 3 - Adding Layers
NNFS Chapter 4 - Activation Functions
NNFS Chapter 5 - Loss
NNFS Chapter 6 - Introducing Optimization
NNFS Chapter 7 - Derivatives
NNFS Chapter 8 - Gradients, Partial Derivatives, and the Chain Rule
NNFS Chapter 9 - Backpropagation
NNFS Chapter 10 - Optimizers
NNFS Chapter 11 - Testing Data
NNFS Chapter 12 - Validation Data
NNFS Chapter 13 - Training Dataset
NNFS Chapter 14 - L1 and L2 Regularization
NNFS Chapter 15 - Dropout
NNFS Chapter 16 - Binary Logistic Regression
NNFS Chapter 17 - Regression
NNFS Chapter 18 - Model Object
NNFS Chapter 19 - A Real Dataset
NNFS Chapter 20 - Model Evaluation
NNFS Chapter 21 - Saving and Loading Model Information
NNFS Chapter 22 - Model Predicting_Inference