Summary of "MIT Introduction to Deep Learning | 6.S191"

Summary of MIT Introduction to Deep Learning | 6.S191 (Lecture 1)

Main Ideas and Concepts

1. Course Introduction and Context

MIT 6.S191 is an intensive one-week boot camp covering fundamental and modern deep learning techniques.
The field of deep learning has rapidly evolved, with dramatic improvements in generative AI and image/video synthesis over the past decade.
A demonstration of real-time voice cloning highlighted the drastic reduction in compute, data, and time requirements compared to earlier efforts (e.g., a 2020 video generation costing $155,000 and hours of data collection).

2. What is Intelligence, AI, Machine Learning, and Deep Learning?

Intelligence: Ability to process information to inform future decisions.
Artificial Intelligence (AI): Algorithms designed to mimic this decision-making process.
Machine Learning (ML): Subset of AI where models learn patterns from data rather than being explicitly programmed.
Deep Learning (DL): Subset of ML using deep neural networks to learn hierarchical features directly from data.

3. Course Structure

Combination of lectures and hands-on software labs using TensorFlow and PyTorch.
Labs include:
- Building a small language model for music generation.
- Facial detection and handling imbalanced data.
- Fine-tuning a large language model (2 billion parameters) and building an AI judge.
Final day includes a project pitch competition with prizes.
Guest lectures from industry leaders.

4. Why Deep Learning and Why Now?

Traditional ML requires manual feature engineering, which is often brittle.
Deep learning automates feature learning in a hierarchical manner (e.g., detecting lines → curves → facial components → faces).
Recent explosion in deep learning progress is driven by:
- Availability of large datasets.
- Powerful and commoditized compute (especially GPUs).
- Open-source software frameworks (TensorFlow, PyTorch).

5. Fundamentals of Neural Networks

Perceptron (Single Neuron):
- Inputs (x_1, x_2, …, x_m) multiplied by weights (w_1, w_2, …, w_m).
- Weighted sum plus a bias term (b).
- Passed through a nonlinear activation function (g(\cdot)) (e.g., sigmoid, ReLU).
Activation functions introduce nonlinearity, enabling neural networks to approximate complex functions.
Multi-output networks are formed by multiple perceptrons (dense layers).
Deep neural networks are created by stacking multiple layers of linear transformations and nonlinearities.

6. Example: Predicting Passing Probability for the Class

Model inputs: Number of lectures attended, hours spent on final project.
Output: Probability of passing (binary classification).
Initial untrained model performs poorly; requires training on historical data.

7. Training Neural Networks

Loss function measures difference between predicted and true outputs.
- Binary classification: softmax cross-entropy loss.
- Regression: mean squared error (MSE).
Goal: Find weights (W) minimizing the loss over the dataset.
Optimization via Gradient Descent:
- Compute gradient of loss w.r.t weights.
- Update weights in the opposite direction of the gradient scaled by learning rate.
Backpropagation computes gradients efficiently using the chain rule.
Practical optimization challenges include:
- High-dimensional, non-convex loss landscapes.
- Setting the right learning rate (too small → slow convergence; too large → instability).
- Adaptive learning rate methods (e.g., Adam) improve training efficiency.

8. Gradient Descent Variants

Full Batch Gradient Descent: Compute gradients over entire dataset (expensive).
Stochastic Gradient Descent (SGD): Compute gradient on a single random data point (noisy but fast).
Mini-batch Gradient Descent: Compute gradients on small batches (e.g., 32 or 128 samples), balancing speed and stability, enabling GPU parallelism.

9. Overfitting and Regularization

Overfitting: Model memorizes training data but performs poorly on unseen data.
Goal: Generalize well to new, unseen data.
Dropout: Randomly zero out activations during training to reduce reliance on any single neuron and encourage multiple pathways.
Early Stopping: Monitor validation loss and stop training when validation loss starts increasing (to avoid overfitting).
Splitting data into training and validation sets is critical for monitoring generalization.

10. Summary of Lecture 1

Understanding neural network architecture (perceptrons, layers, deep networks).
Learning how to optimize neural networks using gradient descent and backpropagation.
Practical considerations: batch sizes, learning rates, overfitting, and regularization.
Next lecture will cover deep sequence modeling, important for large language models.

Methodology / Instructions Presented

Building a Neural Network:
- Define inputs and outputs.
- Initialize weights and biases.
- Compute weighted sums and apply nonlinear activation.
- Stack layers to form deep networks.
Training Procedure:
- Define a loss function appropriate for the task.
- Use backpropagation to compute gradients.
- Update weights via gradient descent (or variants).
- Adjust learning rate carefully, possibly using adaptive schedulers.
- Use mini-batches for efficient and stable training.
- Monitor validation loss for early stopping.
Regularization Techniques:
- Implement dropout by randomly zeroing neuron activations during training.
- Use early stopping based on validation performance.
- Ensure proper train-validation splits.
Software Labs:
- Hands-on exercises with TensorFlow and PyTorch.
- Build models for music generation, facial detection, and large language model fine-tuning.
- Participate in competitions for prizes.

Speakers / Sources Featured

Alexander Amini – Main instructor and presenter of the lecture.
Ava – Co-instructor, to present upcoming lectures (e.g., deep sequence modeling).
Guest Lecturers – Industry leaders presenting state-of-the-art methods (names not specified).
Teaching Assistants (TAs) – Support for labs and student questions.

This summary captures the key educational content, methodologies, and course logistics introduced in the first lecture of MIT’s 6.S191 deep learning course.