Neural Networks Sprint Challenge

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.

Challenge Setup

To get started with the Sprint Challenge, follow these steps:

1. Access the Jupyter notebook using the link below.
2. You can complete the assignment locally or in Google Colab (make sure to Copy to your Google Drive).

Challenge Expectations

The Sprint Challenge is designed to test your mastery of the following key concepts:

• Neural network fundamentals: Understanding neurons, layers, and activation functions
• Simple perceptron implementation: Building and analyzing basic neural network models
• Multi-layer perceptrons: Creating complex architectures with hidden layers
• Model comparison and analysis: Evaluating and comparing different neural network approaches
• Keras implementation: Using the Keras library for practical neural network development

What to Expect

In this sprint challenge, you'll apply everything you've learned about Neural Networks to work with both synthetic and real-world datasets. This challenge will test your ability to:

• Define and explain fundamental neural network concepts and components
• Build simple perceptron models and understand their limitations
• Construct multi-layer perceptrons with appropriate architectures
• Analyze decision boundaries and compare model performance
• Implement neural networks using Keras with proper layer configurations
• Apply hyperparameter tuning using GridSearchCV to optimize model performance
• Work with binary classification tasks using appropriate loss functions and activations
• Present your findings and analysis in a clear, structured manner

The challenge covers all four major neural network components from your modules: architecture, training, tuning, and deployment concepts!

Submission

To submit your Sprint Challenge:

1. Complete all requirements in the Sprint Challenge notebook
2. If using Google Colab, submit the sharing link to your completed notebook
3. If working locally, create a GitHub repository with your Jupyter notebook and submit the repository link
4. Ensure all cells run successfully and outputs are visible before submitting

Sprint Challenge Resources

Neural Network Fundamentals

• Keras API Reference
• TensorFlow: Basic Classification Tutorial
• TensorFlow Playground: Interactive Neural Network Visualization

Model Building and Training

• TensorFlow: Sequential Model Guide
• TensorFlow: Activation Functions
• TensorFlow: Creating Custom Callbacks

Hyperparameter Tuning

• Scikit-learn: Grid Search Documentation
• Introduction to Keras Tuner