Month 7: Neural Networks

Week 1: Introduction to Neural Networks

• Recap of Machine Learning Fundamentals
• Perceptron Model
• Multilayer Perceptron (MLP)
• Activation Functions

Week 2: Loss Functions and Backpropagation Algorithm

• Loss Functions for Neural Networks
• Backpropagation Algorithm
• Gradient Descent
• Stochastic Gradient Descent (SGD)

Week 3: Regularization and Optimization Techniques

• Overfitting and Regularization
• Dropout Regularization
• Batch Normalization
• Weight Initialization
• Optimization Techniques (Adam, RMSProp, etc.)

Week 4: Advanced Neural Network Architectures

• Autoencoders
• Generative Adversarial Networks (GANs)
• Siamese Networks
• Capsule Networks