MIT 6.S191: Introduction to Deep Learning

An introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow.

Course Overview

This course provides an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. The course will cover the fundamental building blocks of deep learning, including:

• Neural network architectures
• Optimization and training
• Convolutional neural networks
• Recurrent neural networks
• Generative models
• Deep reinforcement learning

What You'll Learn

• Understand the foundations of deep learning
• Build and train neural networks
• Apply deep learning to real-world problems
• Use modern deep learning frameworks (TensorFlow/PyTorch)
• Implement state-of-the-art models

Prerequisites

• Basic programming knowledge (Python recommended)
• Understanding of linear algebra
• Familiarity with calculus
• Basic probability and statistics

Course Structure

The course consists of:

• Lecture videos
• Hands-on labs
• Problem sets
• Final project

Ready to start learning? Begin with Chapter 1!