DS Unit 4 Sprint 14: Neural Networks

Welcome to Sprint 14

Welcome to DS Unit 4 Sprint 14, focused on Neural Networks. Neural networks form the foundation of modern deep learning approaches and have revolutionized the field of artificial intelligence. Throughout this sprint, you'll learn the entire neural network lifecycle: from designing architectures to training efficient models, tuning hyperparameters for optimal performance, and deploying models in production environments.

Neural networks excel at handling complex patterns in data that traditional machine learning algorithms struggle with. They've achieved remarkable success in computer vision, natural language processing, time series forecasting, and many other domains. By understanding how to build and deploy neural networks, you'll gain valuable skills that are highly sought after in the data science job market.

Each module in this sprint builds on the previous ones, guiding you through a logical progression from theory to practical implementation to deployment. By the end of this sprint, you'll be able to create, train, and deploy neural networks for real-world applications.

Modules

This sprint is structured to provide you with a comprehensive understanding of neural networks, guiding you through the entire lifecycle from design to deployment:

Module 1

Architect

Learn the foundational components of neural networks including neurons, activation functions, and network topologies. Understand how to design effective architectures and implement them using the Keras Sequential API.

View Module

Module 2

Train

Master the training process for neural networks, including forward propagation, loss calculation, and backpropagation. Implement gradient descent algorithms and understand how to efficiently train networks on various datasets.

View Module

Module 3

Tune

Learn techniques for optimizing neural network performance through hyperparameter tuning. Explore methods to prevent overfitting including regularization, dropout, and early stopping. Implement batch normalization and understand learning rate schedules.

View Module

Module 4

Deploy

Understand the challenges of deploying neural networks in production environments. Learn how to export, save, and serve trained models. Implement techniques for model monitoring and maintenance in real-world applications.

View Module

Course Objectives

By the end of this sprint, you'll be able to:

  • Explain the architecture of neural networks, including neurons, layers, and activation functions
  • Implement feedforward neural networks using the Keras Sequential API
  • Design appropriate network architectures for specific problem domains
  • Apply backpropagation and gradient descent algorithms to train neural networks
  • Evaluate and interpret neural network performance using appropriate metrics
  • Tune hyperparameters including batch size, learning rate, and network depth
  • Implement regularization strategies to prevent overfitting
  • Export and convert trained models for deployment
  • Deploy neural networks in production environments
  • Monitor and maintain deployed models

Sprint Resources

Primary Resources

Code-Along Sessions Sprint Challenge

Additional Resources

TensorFlow Documentation Keras Documentation Deep Learning Book Neural Network Playground Deep Learning Specialization Distill: Interactive ML Articles