A lecture-by-lecture reading of CMU 11-785 Spring 2026 that separates its public 28-lecture teaching sequence from the restricted assignment workflow.
CMU 11-785 Spring 2026 publishes official slides and YouTube recordings for all 28 content lectures, plus extensive bootcamps and recitations. Its HW1–HW4 specifications, starters, and evaluation still depend on Autolab, Piazza, and Kaggle.
Spring 2026 Lecture 1 focuses on neurons, perceptrons, connectionism, and the problem framing of deep learning. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 22 focuses on latent variables, the ELBO, the KL term, and the reparameterization trick. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 3 focuses on data distributions, hypotheses, losses, empirical risk, and their roles in generalization. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 4 focuses on gradients, learning rates, parameter updates, and the training of a linear neuron. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 5 focuses on computational graphs, the chain rule, local derivatives, and gradient reuse. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 6 focuses on non-convex loss surfaces, curvature, saddle points, and momentum's accumulated direction. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 7 focuses on the tradeoffs among full-batch, mini-batch, stochastic gradients, and second-order information. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 8 focuses on AdaGrad, Adam, regularization, BatchNorm, Dropout, and loss selection. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 9 focuses on local connectivity, weight sharing, convolution kernels, and feature maps. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 10 focuses on stride, padding, receptive fields, and multi-channel convolution. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 11 focuses on stacked convolutional architectures, feature hierarchies, and design tradeoffs. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 12 focuses on CNN training, architecture selection, and the end-to-end assembly of a vision model. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 13 focuses on sequence state, temporal unrolling, parameter sharing, and recurrent computation. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 14 focuses on backpropagation through time, gradient stability, and LSTM-style gated memory. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 15 focuses on variable-length input/output, unknown alignment, and the CTC objective. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 16 focuses on blanks, collapse rules, prefix probabilities, and approximate decoding. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 17 focuses on autoregressive factorization, conditional language models, and translation decoding. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 18 focuses on queries, keys, values, scaled dot-product attention, and the Transformer block. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 19 focuses on encoder/decoder structures, masks, residual paths, and architecture variants. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 20 focuses on scaled autoregressive models, training stages, inference, and capability boundaries. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 21 focuses on bottleneck representations, reconstruction objectives, dimensionality reduction, and representation quality. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 22 focuses on latent variables, the ELBO, the KL term, and the reparameterization trick. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 23 focuses on forward noising, reverse denoising, score or noise prediction, and sampling. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 24 focuses on the generator, discriminator, minimax objective, and training instability. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 25 focuses on message passing, aggregation, node representations, and permutation symmetry. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 26 focuses on states, actions, rewards, returns, values, and policy learning. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 27 focuses on associative memory, energy functions, fixed points, and pattern retrieval. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.
Spring 2026 Lecture 28 focuses on energy-based probability models, stochastic units, the partition function, and learning difficulty. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.