Module 2: Train
Module Overview
This module focuses on training neural networks effectively using gradient descent and backpropagation algorithms. You'll learn the fundamental principles behind how neural networks learn from data, including the optimization process that adjusts weights and biases to minimize loss. The module also explores critical hyperparameters like batch size and learning rate that significantly impact model performance and convergence.
Learning Objectives
1. Explain the intuition behind backpropagation and gradient descent
- Understand how gradient descent optimizes the loss function
- Explain the chain rule's role in backpropagation
- Differentiate between types of gradient descent (batch, stochastic, mini-batch)
- Visualize the optimization process across a loss landscape
- Understand the role of loss functions in training and model evaluation
2. Understand the role and importance of batch size
- Define what batch size means in neural network training
- Analyze how batch size affects training speed and memory usage
- Examine the impact of batch size on model convergence
- Implement strategies for selecting optimal batch sizes
3. Understand the role and importance of learning rate
- Define the learning rate parameter and its function
- Analyze how learning rate affects convergence speed and stability
- Compare different optimizers and their learning rate sensitivity
- Diagnose and address common issues related to learning rate selection
Guided Project
Training Neural Networks with Keras
Guided Project File:
DS_422_Train_Lecture.ipynb
Module Assignment
Please read the assignment file in the GitHub repository for detailed instructions on completing your assignment tasks.
Assignment File:
DS_422_Train_Assignment.ipynb
In this assignment, you will continue to build a sketch classification model using the Quickdraw dataset. Your tasks include:
- Comparing model performance with normalized vs. non-normalized data
- Implementing a neural network with specific architecture requirements
- Running experiments with different batch sizes to analyze their impact
- Testing various learning rates and comparing their effects on model convergence
- Experimenting with different optimizers (SGD, Adam) and analyzing results
- Visualizing and interpreting weight distributions using TensorBoard
Assignment Solution Video
Check for Understanding
Complete the following items to test your understanding:
- Explain the key steps of the backpropagation algorithm and its relationship to gradient descent
- Describe how different batch sizes affect neural network training and when to choose larger or smaller batches
- Explain the consequences of setting the learning rate too high or too low
- Compare and contrast different optimization algorithms (SGD, Adam) and their use cases
- Explain why data normalization is important for neural network training
- Describe common loss functions and when to use each type (MSE, categorical cross-entropy, etc.)