MIT 6.S191 Lecture Notes

Lecture 1: Introduction to Deep Learning

Instructor: Alexander Amini

• Course Introduction
  • Fast-paced, one-week course
  • Rapidly changing field over the past 8 years
  • Foundations of AI & Deep Learning

Evolution and Impact of Deep Learning

• Revolutionizing various fields (mathematics, physics, etc.)
• Solving previously unsolvable problems
• Increasingly difficult to teach introductory lectures due to rapid advancements

Deep Learning Example (Introductory Video)

• Hyperrealistic AI-generated video
• Previous examples (few years ago): expensive and time-consuming
• Current capabilities: generate media content directly from English text
• Example: Generating content, code, etc., using deep learning

Course Structure

• Technical Lectures:
  • Foundation of neural networks
  • Perceptron as the building block
  • Overview of the course topics
  • Neural networks, backpropagation, optimization
• Software Labs:
  • Practical experience after each lecture
  • Music generation, computer vision, large language models
  • Final project pitch competition with prizes

Key Concepts

• Intelligence: Ability to process information for decision making
• Artificial Intelligence (AI): Computer processing of information
• Machine Learning (ML): Subset of AI, learning from data
• Deep Learning: Subset of ML using neural networks

Neural Networks & Perceptrons

• Perceptron: Basic unit of a neural network
  • Inputs, weights, bias, non-linear activation function
  • Learning from raw data to make decisions
• Activation Functions: Non-linear (e.g., Sigmoid, ReLU)
  • Allows networks to capture complexities

Building Neural Networks

• Forward Propagation: Information flow through network
• Layers: Input, hidden, output layers
  • Fully connected layers
• Implementation: Using libraries like TensorFlow, PyTorch (example code provided)

Real-World Application Example: Predicting Class Pass/Fail

• Neural network with inputs (lectures attended, project hours)
• Importance of training the model with data

Training Neural Networks

• Loss Function: Difference between predicted and actual values
  • Softmax for classification, mean squared error for regression
• Gradient Descent: Optimization algorithm
  • Steps: Compute gradient, take step in opposite direction, iterate

Backpropagation

• Algorithm: Chain rule to compute gradients
  • Efficiently computing partial derivatives
  • Libraries handle backprop (e.g., TensorFlow)

Challenges in Training Neural Networks

• Optimization
  • Complex and computationally intensive
  • Proper initialization and setting of hyperparameters (e.g., learning rate)
  • Mini-batch gradient descent for faster convergence

Regularization Techniques

• Dropout: Randomly setting neuron outputs to zero during training
• Early Stopping: Monitoring training to prevent overfitting

Conclusion

• Overview of neural network building and training
• Upcoming lectures on sequence modeling and Transformers