Neural Networks Sprint Challenge

Overview

This sprint challenge will test your understanding of neural networks, including architecture, training, tuning, and regularization. You'll need to apply the concepts you've learned throughout the sprint to design, implement, and optimize neural network models for different classification tasks.

The challenge consists of three main parts:

  1. Simple Perceptron Implementation: Build and analyze a simple perceptron model.
  2. Multi-Layer Perceptron: Create a more complex neural network with multiple hidden layers.
  3. Keras Implementation with Hyperparameter Tuning: Implement a multilayer perceptron using Keras and optimize it through hyperparameter tuning.

Study Guide

What You Need to Know

To successfully complete this Sprint Challenge, you should be familiar with:

  • Neural network architecture and components (neurons, layers, activation functions)
  • The Keras Sequential API for building neural networks
  • Hyperparameter tuning techniques
  • Regularization strategies for neural networks
  • Model evaluation and comparison

Key Concepts to Review

  • How perceptrons differ from multi-layer neural networks
  • The role of activation functions in neural networks
  • Best practices for model evaluation in classification tasks
  • Techniques for visualizing decision boundaries
  • Implementation of callbacks for training optimization

Challenge Setup

The challenge uses two datasets:

  1. A synthetic dataset for the perceptron and MLP comparison tasks
  2. The Heart Disease dataset for the Keras implementation task

Access the Challenge Notebook

You can access the Sprint Challenge notebook here:

Sprint Challenge Notebook

Requirements

Part 1

Review Neural Network Concepts

Demonstrate your understanding of key neural network concepts, including:

  • Neuron structure and function
  • Different types of layers (input, hidden, output)
  • Activation functions and their purpose

Part 2

Perceptron Implementation and Analysis

In this section, you will:

  1. Build a simple perceptron with Keras
  2. Create a multi-layer perceptron with custom parameters
  3. Analyze and compare the performance of both models
  4. Visualize and interpret decision boundaries

Part 3

Keras MLP with Hyperparameter Tuning

For this part, you will:

  1. Implement a multilayer perceptron using Keras
  2. Prepare the Heart Disease dataset for classification
  3. Conduct hyperparameter tuning using GridSearchCV
  4. Report and interpret model performance results

Submission Requirements

Before submitting your completed notebook, ensure that you:

  1. Restart the kernel
  2. Run all cells sequentially, from top to bottom
  3. Check that all cells have executed without errors
  4. Verify that all required outputs and visualizations are displayed

Additional Resources

Helpful Documentation

Keras API Reference TensorFlow: Basic Classification Tutorial Scikit-learn: Grid Search Documentation TensorFlow: Creating Custom Callbacks TensorFlow Playground: Interactive Neural Network Visualization