MIT 6.S191 Lecture 1: The Minimal Structure of Deep Learning
Lecture 1 of the 2026 course builds the vocabulary shared by the rest of the course: perceptrons, forward propagation, loss, and gradient descent.
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12 postsMIT 6.S191 Lecture 2: Sequence Modeling: From RNNs to Attention
Lecture 2 of the 2026 course addresses data where order changes meaning—text, audio, and time series—and connects directly to music generation in Lab 1.
MIT 6.S191 Lecture 3: Computer Vision: How Convolution Preserves Spatial Structure
Lecture 3 of the 2026 course moves from image tensors, convolution, and pooling to recognition systems, preparing for MNIST and face detection in Lab 2.
MIT 6.S191 Lecture 4: Generative Modeling: From Latent Spaces to Diffusion
Lecture 4 of the 2026 course separates generative from discriminative tasks, organizes VAE, GAN, and diffusion objectives, and leads into Lab 2’s DB-VAE.
MIT 6.S191 Lecture 5: Reinforcement Learning: Learning from Return Instead of Labels
Lecture 5 of the 2026 course connects agent, environment, state, action, reward, and policy into an interaction loop, introducing credit assignment and exploration.
MIT 6.S191 Lecture 6: New Frontiers: Choosing the Problem Beyond the Model
Lecture 6 of the 2026 course places deep learning in emerging applications and real constraints, emphasizing data, outputs, evaluation, and failure conditions.
MIT 6.S191 Lecture 7: The Three Laws of AI: Safety Through Observability and Evaluation
Lecture 7 of the 2026 course starts from Asimov’s literary laws and examines modern safety protocols through traces, test data, and continuous evaluation.
MIT 6.S191 Lecture 8: AI for Science: Putting Domain Structure into Learning
Lecture 8 of the 2026 course uses the scientific-discovery loop to show how simulators, AI emulators, and experiments cooperate instead of reducing science to generic prediction.
MIT 6.S191 Lecture 9: Massively Parallel Training: Memory and Communication Set the Boundary
Lecture 9 of the 2026 course starts with GPU memory pressure and moves through checkpointing, offloading, ZeRO, FSDP, and multiple forms of parallelism.
MIT 6.S191 Lab 1: Generate Music with PyTorch and an LSTM
In the 2026 lab, students cover tensors, autograd, and modules before turning ABC notation into character sequences for LSTM music generation.
MIT 6.S191 Lab 2: From MNIST to Facial Debiasing with a DB-VAE
In the 2026 lab, part 1 classifies MNIST with dense and convolutional networks; Part 2 learns a facial latent distribution with a DB-VAE and changes training sampling.
MIT 6.S191 Lab 3: LoRA Fine-Tuning and LLM-as-a-Judge Evaluation
In the 2026 lab, students build chat templates and generation with LFM2-1.2B, adapt style through LoRA, and combine OpenRouter with Opik for a judge workflow.