Neural Networks: Forward Propagation and Backpropagation - Lecture 25

Overview

Dive into neural network training with forward propagation and backpropagation techniques for parameter optimization in this comprehensive exploration.

Syllabus

• Introduction to Neural Networks
Overview of neural network architectures
Importance of forward and backward propagation in training
• Forward Propagation
Explanation of forward propagation
Mathematical model and computation in feedforward networks
Activation functions and their roles (ReLU, Sigmoid, Tanh)
Example of forward propagation in a simple neural network
• Backpropagation
Concept of backpropagation as a method for training networks
The chain rule in calculus and its application in neural networks
Deriving error gradients with respect to weights
Practical steps in backpropagation execution
Example of backpropagation in action
• Parameter Optimization
Loss functions and their significance (MSE, Cross-Entropy)
Introduction to gradient descent algorithm
Variations of gradient descent (Stochastic, Mini-batch)
Hyperparameters tuning (learning rate, epochs)
• Implementation and Practice
Coding forward and backpropagation from scratch in Python
Utilizing libraries (TensorFlow, PyTorch) for neural networks
Hands-on exercise: Train a simple neural network model
• Common Pitfalls and Best Practices
Overfitting and underfitting issues
Regularization techniques (L1, L2, Dropout)
Tips for debugging neural network models
• Conclusion and Q&A
Key takeaways from forward and backpropagation processes
Open floor for questions and further discussions

Subjects

Computer Science