DS Unit 4 Sprint 15

Course Overview

Welcome to DS Unit 4 Sprint 15, the culmination of your core data science curriculum. This sprint explores specialized neural network architectures that power today's most advanced AI applications in computer vision, natural language processing, and time series forecasting.

You'll master key architectures designed for specific data types: Recurrent Neural Networks (RNNs) and LSTMs for sequential data, Convolutional Neural Networks (CNNs) for images, and AutoEncoders for unsupervised learning and dimensionality reduction. Building on your foundation in feed-forward networks, these powerful tools will equip you to tackle complex real-world problems using cutting-edge deep learning techniques.

Modules

This sprint is structured to explore specialized neural network architectures for different data types and application domains:

Module 1

Recurrent Neural Networks and LSTM

Explore neural networks designed specifically for sequential data. Learn how RNNs overcome the limitations of feed-forward networks when handling sequences, and how LSTMs solve the vanishing gradient problem.

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Module 2

Convolutional Neural Networks

Discover the architecture that revolutionized computer vision. Learn how convolutional layers mimic the visual cortex through specialized filters, how pooling layers create spatial hierarchies.

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Module 3

AutoEncoders and Generative Models

Master unsupervised learning techniques with autoencoders, neural networks that learn efficient data encodings without labeled data. Understand encoder and decoder components for information retrieval.

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Module 4

Time Series Forecasting

Apply deep learning to time-dependent data for forecasting future values. Learn specialized techniques for preprocessing time series data and implementing LSTM networks for prediction tasks.

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Code-Alongs

This sprint features two code-alongs to help you master advanced deep learning concepts through hands-on practice:

Code-Along 1

LSTM Text Generation

Build a text generation system using LSTM networks. Learn how to preprocess text data, design and train a character-level language model, and generate new text sequences.

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Code-Along 2

Variational AutoEncoders

Explore advanced autoencoder architectures with variational autoencoders (VAEs). Learn how to implement VAEs for generative modeling and understand the mathematics behind their latent space representations.

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Course Objectives

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

  • Explain the architecture and applications of recurrent neural networks and LSTM networks
  • Implement sequence modeling for text generation using LSTM networks
  • Describe convolution and pooling operations in neural networks
  • Build convolutional neural networks for image classification tasks
  • Utilize pre-trained CNN models through transfer learning
  • Understand the components of autoencoders and their applications
  • Train autoencoders for dimensionality reduction
  • Apply autoencoders to information retrieval problems
  • Implement deep learning models for time series forecasting
  • Compare and select appropriate neural network architectures for different problem domains
  • Articulate the differences between AI, ML, and AGI
  • Recognize ethical challenges in AI development and deployment

Additional Resources

Deep Learning Resources

Distill: Attention and Augmented RNNs A Guide to CNNs Building Autoencoders in Keras TensorFlow Playground Deep Learning Book