MIT Course on AI and Deep Learning

Introduction

• Instructor: Alexander Amini
• Course aims to provide foundational understanding of AI and deep learning.
• Fast-paced, intensive one-week course.
• Deep learning rapidly evolving; difficult to keep course current.

Overview of AI and Deep Learning

• AI solving previously unsolvable problems.
• Deep learning is impacting multiple fields: robotics, medicine, etc.
• The evolution of AI has allowed for hyper-realistic content creation.

Technological Advances

• AI content generation has become more accessible.
• Models now generate media from English prompts without coding.
• Ability to create software with AI is increasing.

Course Goals

• Teach foundations to create AI and deep learning models.
• Distinction between intelligence, artificial intelligence, and machine learning:
  • Intelligence: Processing information for future decisions.
  • Artificial Intelligence: Computers processing information like humans.
  • Machine Learning: Teaching computers decision-making from data.
  • Deep Learning: Subset using neural networks for processing data.

Course Structure

• Divided into technical lectures and software labs.
• Labs include practical projects like music generation, computer vision.

Key Concepts in Deep Learning

Neural Networks

• Foundation: perceptron or single neuron.
• Composed of weights and biases.
• Use activation functions (sigmoid, relu) to introduce nonlinearity.
• Important for handling nonlinear data.

Training Neural Networks

• Neural networks learn from data (like a baby learning).
• Training involves minimizing loss functions like cross entropy.
• Use gradient descent for optimization.

Overfitting and Regularization

• Overfitting: Model performs well on training data, poorly on test data.
• Regularization techniques:
  • Dropout: Randomly zeroing out neurons during training.
  • Early stopping: Stopping training when testing accuracy declines.

Optimization Techniques

• Gradient descent used to find optimal weights.
• Learning rates impact training speed and accuracy.
• Use of mini-batches to speed up training and reduce compute load.

Practical Aspects

• Importance of adapting learning rates and using GPU for parallel computation.

Conclusion

• Course provides building blocks for creating deep learning models.
• Next lecture to cover sequence modeling and Transformer models.