MIT Course 6.S191: Introduction to Deep Learning

Introduction

• Instructor: Alexander Amini
• Co-Instructor: Ava
• Fast-paced, intensive one-week course covering deep learning foundations.

Overview of Deep Learning

• Rapid advancements in AI and deep learning over the past decade.
• AI solving previously unsolvable problems, even outperforming humans.
• Course lectures increasingly challenging due to the fast-paced nature of the field.
• Example: AI-generated introductory video that went viral, showcasing realism of AI-generated content.

Current State of AI

• Deep learning is now accessible and can generate hyper-realistic content from simple prompts.
• Models today can generate content, write code, and educate users on the coding process.

Course Structure

• Technical Lectures and Software Labs
  • Daily technical lectures covering foundational concepts: starting with neural networks and basic building blocks (perceptron).
  • Software Labs to reinforce learnings and apply concepts through projects.
  • Guest lectures from industry leaders to showcase real-world applications of deep learning.

Lab Schedule

1. Lab 1: Music Generation
   • Build a neural network to compose new songs.
2. Lab 2: Computer Vision
   • Create a facial detection system and address biases in detection.
3. Final Lab: Large Language Models
   • Fine-tune a multi-billion parameter language model for a chatbot.
4. Project Pitch Competition
   • Present projects in a Shark Tank-style format with prizes.

Foundations of Deep Learning

• Intelligence Definition: Ability to process information to inform decisions.
• Artificial Intelligence: Computers processing information like humans.
• Machine Learning: Teaching computers to process information from data rather than hardcoding rules.
• Deep Learning: Subset of machine learning using neural networks for raw data processing.

Key Concepts

• Perceptron: Building block of neural networks.
  • Inputs multiplied by weights, summed, and passed through an activation function.
  • Activation functions (e.g., sigmoid, ReLU) introduce non-linearity to the model.
• Non-linearity is crucial for dealing with complex, real-world data.

Neural Network Architecture

• Neural networks consist of multiple neurons (perceptrons) organized in layers: input layer, hidden layers, output layer.
• Each layer has its own weights and biases, and uses non-linear activation functions.
• Process flow: input → weighted sum (dot product) + bias → activation function → output.

Training Neural Networks

• Loss Function: Measures how well the model performs on training data; guides learning.
• Gradient Descent: Optimization algorithm for adjusting weights to minimize loss.
  • Compute gradient of loss, update weights iteratively.
  • Backpropagation: Efficiently computes gradients using the chain rule.
• Stochastic Gradient Descent (SGD): Uses mini-batches of data instead of the full dataset for faster training.

Overfitting and Regularization

• Overfitting: Model learns training data too well, failing to generalize to unseen data.
• Regularization Techniques:
  • Dropout: Randomly deactivate neurons during training to prevent reliance on any single feature.
  • Early Stopping: Stop training when model performance on validation data starts to decline.

Conclusion

• Summary of core topics covered: perceptrons, neural networks, training processes, and practical considerations for model optimization.
• Next lecture will cover deep sequence modeling using RNNs and the Transformer model.

Acknowledgments

• Thanks to sponsors for supporting the course.