MIT 6.S191 Introduction Lecture Notes

Instructor Introduction

• Instructor: Alexander Amini
• Co-instructor: Ava
• Course Title: MIT 6.S191
• Overview: Fast-paced, one-week course covering foundational and advanced concepts in AI and deep learning
• Course Evolution: Rapidly changing course content due to the dynamic nature of the field

Importance of AI and Deep Learning

• Revolutionized various fields like robotics, medicine, mathematics, and physics
• Solving problems previously considered unsolvable within human lifetimes
• AI models have evolved to solve complex tasks beyond human performance

Course Format and Objective

• Combination of technical lectures and software labs
• Aim: Build foundational understanding and the ability to create state-of-the-art AI models from scratch
• Final Project: Project pitch competition with prizes

Course History and Updates

• Previous introduction videos went viral for showcasing AI's capabilities
• Example: AI-generated content previously required expensive computational resources
• Present: Simplified AI model training accessible on everyday devices (smartphones) using natural language prompts

Core Concepts To Be Covered

1. Intelligence: Ability to process information and inform future decision-making
2. Artificial Intelligence: Computer's ability to process information and inform decisions
3. Machine Learning: Subset of AI, programming computers to process information from data
4. Deep Learning: Subset of Machine Learning, uses neural networks to process raw data

Neural Networks and Perceptrons

• Perceptrons: Basic units of neural networks
  • Inputs: Multi-dimensional (X1, X2, ..., Xm)
  • Weights: (W1, W2, ..., Wm)
  • Bias: Shift activation function horizontally
  • Nonlinearity: Activation functions (e.g., sigmoid, ReLU) to introduce nonlinearity
• Activation Functions:
  • Sigmoid: Squeezes outputs between 0 and 1, useful for probability
  • ReLU: Rectified linear unit, commonly used due to computational efficiency

Neural Networks Structure

• Layers: Composed of neurons; input layer, hidden layers, and output layer
• Deep Networks: Stacking multiple layers to increase model complexity
• Fully Connected Layers: Each neuron connected to each input from previous layer
• Training Neural Networks: Utilize software tools like TensorFlow for easy implementation

Gradient Descent and Backpropagation

• Gradient Descent: Optimization algorithm used to minimize the loss function
  • Steps: Initialize weights, compute gradient, update weights, repeat until convergence
• Loss Function: Measure of how far predictions are from the ground truth
  • Cross-Entropy: Used for classification tasks
  • Mean Squared Error: Used for regression tasks
• Backpropagation: Algorithm to compute the gradient of the loss function with respect to weights
  • Uses chain rule of calculus to propagate errors back through the network

Practical Insights on Training Neural Networks

• Learning Rate: Critical parameter that affects convergence speed and quality
  • Too high: Risk of divergence
  • Too low: Slow convergence
  • Adaptive methods recommended
• Stochastic Gradient Descent (SGD): Use of mini-batches for more efficient and scalable training
  • Mini-batch size commonly around 32 samples for balance between accuracy and computational efficiency
• Parallelization: Leveraging GPUs for faster computation

Overfitting and Regularization

• Overfitting: Model performs well on training data but poorly on unseen data
• Regularization Techniques:
  • Dropout: Randomly setting activations to zero during training to prevent overfitting
  • Early Stopping: Monitor model performance on validation set and stop training when performance worsens

Summary and Next Steps

• Key Takeaways: Understanding perceptrons, neural network structure, optimization, and regularization
• Next Lecture: Deep sequence modeling using RNNs and Transformers
• Break: Brief five-minute pause before next lecture by Ava