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 2 focuses on Boolean functions, how network depth and width affect representational capacity (including a multi-input XOR example), and the hand-off at the end to the next lecture's training problem. 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 how the learning problem is set up (representing inputs and outputs as numbers), the perceptron learning rule and why it does not carry over to multi-layer networks, differentiable divergences, and empirical risk minimization (the loss). 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 the definitions of derivative and gradient, extrema and second derivatives, gradient descent as repeated steps against the gradient, and the network, input/output representations, and divergence that must be defined before training. 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 layered-network notation, the forward pass, the chain rule and Jacobians (including vector activations), and how the backward pass computes weight and bias derivatives layer by layer. 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 convergence of gradient descent (step-size choice on convex and quadratic functions, differences between dimensions), local minima and saddle points, decaying or adaptive learning rates, R-prop, and momentum with Nesterov accelerated gradient. 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 incremental SGD and mini-batch updates and trend-based updates such as RMSprop and momentum (second-order methods are only in the slides, not in the recording). 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 the choice of divergence (loss), BatchNorm, dropout and other regularization methods, and training tricks such as early stopping (AdaGrad and Adam are not in this recording). 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 why we scan the input, distributing the scan over several layers, handling deformation with max pooling, and the CNN / time delay neural network structure. 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 where the CNN comes from (Hubel and Wiesel, Fukushima's Neocognitron), the operations of convolution and pooling layers, and design choices such as filter size, stride and number of channels. 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 a recap of CNNs and backpropagation through convolution and pooling layers. 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 additional backpropagation rules (strides, upsampling and downsampling), invariance to transformations other than shifts, and a quick history of CNNs with the major architectures. 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 why sequences need their own models, state and memory, Jordan/Elman-style recurrent networks, backpropagation through time, and bidirectional networks. 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 the alignment problem when training sequence models (training with explicit or re-estimated alignments, and the risk of local optima); CTC is only introduced at the very end, with details left to the next lecture. 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 blank symbols and the merging of adjacent repeated symbols, CTC's forward (alpha) probability computation, and pruned approximate decoding (beam search is described only briefly near the end). 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 next-word-prediction language models, conditional translation models, and beam-search 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 sequence-to-sequence attention (queries, keys, values, and softmax-weighted averaging), a mention of multi-head attention, and a brief introduction to the Transformer. 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, pre-training and scaling laws, and post-training (including improving the model from its own sampled answers and feedback). 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 what neural networks actually represent, the reconstruction objective (autoencoders), and the link to PCA (dimensionality reduction). 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 (the Gibbs distribution and its normalizing constant Z), stochastic neurons, and how to train such probabilistic networks. This guide follows the official slides and recording and adds a small self-check that does not depend on the enrolled-course grader.