MIT Introduction to Deep Learning | 6.S191

Science & Technology

Introduction

Good afternoon, everyone, and welcome to MIT 6.S191. My name is Alexander Amini, and I’ll be one of your instructors for the course this year along with Ava. Together, we are really excited to welcome you to this incredible course. This is a very fast-paced and intense one-week course that will cover the foundations of a very fast-paced moving field: deep learning. This field has been rapidly changing over the past eight years that we have taught this course at MIT.

The Evolution of AI and Deep Learning

Over the past decade, the field of AI and deep learning has been revolutionizing various domains such as science, mathematics, physics, and more. Challenges that we did not think were solvable in our lifetimes are now being tackled by AI and deep learning at performance levels that surpass human capabilities.

A Glimpse into the Past

A few years ago, when we introduced this course, we showcased a video where the entire speech and video were generated using deep learning and artificial intelligence. This video, which cost about $ 10,000 in compute to generate just a minute-long clip, was very impressive at the time. Fast forward to today, AI can generate content end-to-end directly from English language instructions without the need for prior coding.

Currently, deep learning models have become so commoditized that they allow the generation of hyperrealistic media and even code snippets from simple English prompts. For instance, you can ask a deep learning model to write TensorFlow code to train a neural network or generate photos of an astronaut riding a horse.

Executing and Creating using AI

This course is designed to teach you the foundations of how these AI and deep learning systems are built from the ground up. By the end of the week, you will have learned to create new types of deep learning models using these foundational principles.

What Are We Going to Learn?

Deep learning can be overwhelming due to its rapid advancements. However, understanding the key concepts is essential. We start by asking, "What is intelligence?" followed by what constitutes artificial intelligence and machine learning, and finally, deep learning within machine learning.

Breaking Down Deep Learning

  1. Artificial Intelligence (AI): The ability to process information and inform future decisions using computers.
  2. Machine Learning (ML): A subset of AI that uses data to teach computers to process information and make decisions.
  3. Deep Learning (DL): Uses neural networks for processing unprocessed data, allowing large datasets to inform decisions.

In this course, you will learn how to:

  • Process data and information using deep learning.
  • Create functioning deep learning models.
  • Implement these models in software labs.

The syllabus includes technical lectures and practical software labs to reinforce learnings and culminates in a project pitch competition.

Foundations of Neural Networks

The building block of a neural network is a perceptron, which processes information and generates an output. The perceptron ingests inputs, multiplies them by weights, sums them, and passes the resultant value through a non-linear activation function.

Understanding the Activation Functions

Commonly used activation functions include the sigmoid function, tanh, and ReLU. These functions introduce nonlinearity, allowing the neural networks to address complex, nonlinear data sets.

Neural Networks and Forward Propagation

We covered one-layer and multi-layer neural networks, how weights are initialized, and how they connect through layers to form deep neural networks. Practical implementation using TensorFlow was also discussed.

Training Neural Networks

Key steps in training neural networks involve computing the loss function and adjusting the weights using gradient descent. The lecture then delved into:

  1. Loss Function: E.g., cross-entropy for binary classification.
  2. Gradient Descent: Optimization algorithm to minimize loss by adjusting the model's weights.
  3. Backpropagation: Algorithm for computing gradients.

Practical Training Tips

Techniques such as using mini-batches, adaptive learning rates, and regularization methods like Dropout and early stopping were discussed to prevent overfitting and to optimize neural network training.

Conclusion

We covered:

  • Fundamentals of neural networks.
  • Mathematical optimization of these systems.
  • Practical training tips and techniques.

In the next lecture, Ava will dive into deep sequence modeling, including RNNs and the Transformer architecture.

Thank you for joining, and we will resume in about five minutes.

Keywords

  • Deep Learning
  • Artificial Intelligence
  • Machine Learning
  • Neural Networks
  • Perceptron
  • Activation Functions
  • Gradient Descent
  • Loss Function
  • Backpropagation
  • Mini-batches
  • Dropout
  • Overfitting

FAQ

Q: What is the fundamental building block of a neural network?
A: The fundamental building block of a neural network is a perceptron, which processes inputs, multiplies them by weights, sums them, and applies a non-linear activation function.

Q: Why is nonlinearity important in neural networks?
A: Nonlinearity allows neural networks to handle complex, nonlinear data, making them more expressive and capable of capturing intricate patterns.

Q: What is gradient descent?
A: Gradient descent is an optimization algorithm used to minimize the loss function by iteratively adjusting the model's weights in the opposite direction of the gradient.

Q: What is backpropagation?
A: Backpropagation is an algorithm used for computing the gradient of the loss function with respect to each weight in the neural network, a crucial step for updating the weights during training.

Q: What are some techniques to prevent overfitting?
A: Techniques to prevent overfitting include Dropout, early stopping, and regularization methods, which help the model generalize better on unseen data.