MIT 6.S191: Introduction to Deep Learning

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Course Introduction

• Instructors: Alexander Amini and Ava
• Course Duration: One week, fast-paced and intense
• Focus: Foundations of AI and deep learning, a rapidly evolving field
• Context: AI is revolutionizing fields like science, mathematics, physics, etc.
• Lecture Dynamics: Introduction lectures in AI are changing rapidly unlike other subjects.

Deep Learning Overview

• Historical Context: AI has solved problems beyond human capabilities.
• Content Creation Example: A video generated by AI was made at a cost of $10,000, which became viral.
• Current State: AI content creation has become common and accessible using language prompts.

What is Intelligence?

• Definition: Ability to process information to inform future decision-making
• Artificial Intelligence: Giving computers the ability to process info like humans
• Machine Learning: Teaching computers to process data and make decisions without hardcoding
• Deep Learning: Using neural networks to process large datasets

Course Structure

• Components: Technical lectures and software labs
• Topics: Foundations of neural networks, perceptron, application of deep learning
• Learning Goals: Build and apply neural networks, understand and deploy AI models
• Labs: Music generation, computer vision, language models, project pitch competition

Foundations of Deep Learning

• Deep Learning: Shift from hand-engineered features to learning from raw data
• Neural Networks: Built from perceptrons (neurons), use nonlinear activation functions
• Activation Functions: Sigmoid, ReLU, introduce non-linearities to handle complex data
• Model Complexity: Neural networks vary in depth and number of parameters

Neural Network Architecture

• Perceptron: Basic unit of neural networks, involves dot product, bias, and non-linearity
• Layer Construction: Layers of neurons with weights and biases
• Programming Neurons: Using libraries like TensorFlow to implement layers and networks
• Deep Networks: Stacking layers to create more complex models

Training Neural Networks

• Learning Process: Define inputs, compute loss, adjust weights using gradient descent
• Loss Functions: Softmax for classifications, mean squared error for regression
• Optimization: Gradient descent and its variants like stochastic gradient descent
• Challenges: Finding the right learning rate, handling large datasets

Techniques for Neural Network Optimization

• Batching: Use mini-batches to improve computation efficiency
• Regularization: Techniques like dropout to avoid overfitting
• Early Stopping: Prevent training beyond the optimal point to avoid overfitting

Practical Considerations

• Loss Landscape: Real-world networks have complex landscapes
• Adaptation: Using adaptive learning rates and optimizers
• Parallelization: GPU utilization for faster computations

Closing Remarks

• Conclusion: Overview of neural networks and optimization
• Next Steps: Upcoming lectures on RNNs and transformers, focusing on sequence modeling

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Additional Resources

• Presentations and slides available online
• Support through Piazza and teaching team