Learn Deep Learning with NumPy

Full Outline

• 0: Introduction
• 1.1: Getting Started with NumPy Arrays
• 1.2: Matrix Operations for Neural Networks
• 1.3: Mathematical Functions and Activation Basics
• 2.1: Understanding Loss Functions
• 2.2: Gradient Descent for Optimization
• 2.3: Mini-Batch Gradient Descent
• 2.4: Debugging with Numerical Gradients
• 3.1: Single-Layer Perceptrons
• 3.2: Activation Functions for Neural Networks
• 3.3: Multi-Layer Perceptrons and Forward Propagation
• 3.4: Backpropagation for Training MLPs
• 4.1: Deeper MLPs and Vanishing Gradients
• 4.2: Convolutional Layers for CNNs
• 4.3: Pooling and CNN Architecture
• 4.4: Regularization Techniques
• 4.5: Advanced Optimization and Capstone
• 5: Conclusion