September 2026 Release

This release narrows the project around deep-learning fundamentals and large-language-model systems, expands the Chinese LLM training engineering curriculum into a complete ten-part chapter, and adds new material on chapter exercises, as well as Stanford CS224N. It also extends dnnlpy with GPT-2 and gensim-compatible utilities, simplifies the package surface, and introduces a secure Netlify preview workflow for pull requests.

New Notebooks

Chapter 12: LLM Training Engineering

• 12.2 FLOPs, Memory, and Arithmetic Intensity: Why Compute Alone Is Not Enough
• 12.3 Profiling: How to Find Where Code Is Slow
• 12.4 Mixed Precision: FP32, FP16, BF16, and Loss Scaling
• 12.5 Gradient Accumulation: Simulating Large Batches When Memory Is Limited
• 12.6 Activation Checkpointing: Trading Recomputation for Memory
• 12.7 Modern Attention APIs and Hugging Face Kernels
• 12.8 Introduction to Triton: When to Write Your Own Kernel
• 12.9 Introduction to Distributed Training: Intuition for DDP, ZeRO, and FSDP
• 12.10 Large-Model Checkpointing: Recovering Model, Optimizer, and Distributed State

Exercises

• 1.6 Chapter 1 Exercises: Introduction to Deep Learning
• 2.8 Chapter 2 Exercises: PyTorch Fundamentals and the Training Workflow
• 3.9 Chapter 3 Exercises: Multi-Layer Perceptrons
• 4.10 Chapter 4 Exercises: Optimization Algorithms
• 5.7 Chapter 5 Exercises: Convolutional Neural Networks
• 6.9 Chapter 6 Exercises: Regularization and Normalization
• 9.12 Chapter 9 Exercises: Attention and Transformers
• 11.8 Chapter 11 Exercises: Implementing GPT-2 from Scratch
• 12.11 Chapter 12 Exercises: LLM Training Engineering

Stanford CS224N

• Added a dedicated CS224N course section and website navigation.
• Added the Assignment 3 write-up, handout, training configuration, training script, and supporting figures.
• Added a GPT-2 implementation with small, medium, large, and XL configurations.
• Added pretrained Hugging Face weight conversion, text generation, language-modeling loss, and TinyStories training support.
• Added minimal gensim-compatible downloading, KeyedVectors, similarity, and Word2Vec-format loading utilities for the course exercises.
• Added reference snapshots and tests for the GPT-2 and word-vector implementations.

dnnlpy Package Updates

• Added the CS224N GPT-2 model, configuration, pretrained-weight conversion, generation, and training utilities.
• Added lightweight gensim-compatible downloader, keyed-vector, similarity, and Word2Vec-format APIs.
• Refined multi-head attention, scaled dot-product attention, and Transformer implementations and documentation.
• Improved GPT-2 pretrained loading to support all standard model sizes.
• Optimized normalization statistics with torch.var_mean.
• Removed Local Response Normalization and its public exports.
• Removed the device-memory helpers in favor of PyTorch accelerator APIs.
• Removed the ViT, VAE, and DDPM model implementations to match the project’s revised scope.
• Refactored neural-network tests around reusable lowercase dimension fixtures and broader parameterization.
• Expanded tests for attention, FlashAttention, Transformers, GPT-2, keyed vectors, and Word2Vec-format loading.
• Updated package support for PyTorch 2.14 and Transformers 5.18 and standardized the uv PyTorch backend on CPU.

Book and Documentation Updates

• Adopted DNEP 2, narrowing the book to deep-learning fundamentals and large-language-model topics.
• Removed the Vision Transformer, autoencoder and VAE, and diffusion-model chapters from the current book structure.
• Renumbered Regularization and Normalization from Chapter 7 to Chapter 6.
• Renumbered Implementing GPT-2 from Scratch from Chapter 18 to Chapter 11.
• Renumbered LLM Training Engineering from Chapter 19 to Chapter 12 and expanded it to ten instructional sections plus exercises.
• Updated the English and Chinese navigation, part structure, chapter listings, metadata, references, figures, and README.md files for the revised organization.
• Rewrote the introduction to regularization and normalization and refined optimization, attention, and LLM engineering explanations.
• Added Colab links to the new exercise chapters and restored missing chapters in the Typst configuration.
• Added repository guidelines and a dedicated workflow for synchronizing Chinese Quarto documentation to English.
• Added DNEP proposal and improvement-request templates and refreshed the pull-request and issue templates.

Build and Development Updates

• Updated the project dependencies for PyTorch 2.14, TorchVision 0.29, Transformers 5.18, TorchCodec 0.17, Triton 3.8 on Linux, xFormers 0.0.35, and related libraries.
• Added TorchCodec and removed unused notebook, Seaborn, Weights & Biases, Rich Click, and Windows-specific Triton dependencies.
• Simplified the Docker environment and updated Typst page-break and table styling.
• Improved device detection, profiler trace naming, SVG backgrounds, figure rendering, and high-performance-kernel compatibility checks.

CI Updates

• Added Netlify deployment previews for pull requests with GitHub deployment registration, status reporting, and sticky preview comments.
• Added a fixed metadata artifact that securely connects unprivileged Quarto renders to the privileged deployment workflow.
• Added validation for run IDs, head SHAs, repository identity, pull-request state, and preview metadata before deployment.
• Added automated tests for the Netlify preview metadata and routing logic.
• Improved concurrency handling so newer preview deployments supersede stale runs.
• Enabled cancellation of superseded CI runs and simplified Quarto caching and browser setup.
• Updated Ruff pre-commit hooks and refreshed workflow permissions and artifact handling.

Merged Pull Requests

• [zh] MNT: Add simulated hidden state in shape demonstration example by @wqpwqp1222 in #16
• DEP: Update dependency regex to >=2026.8.31,<2026.9.0 by @renovate[bot] in #18
• DEP: Update dependency regex to >=2026.9.3,<2026.10.0 by @renovate[bot] in #21
• DEP: Update dependencies torch and torchvision to 2.14.0/0.29.0 by @renovate[bot] in #25
• [DNEP 1] Update torch-backend to CPU and remove unused CUDA configurations by @jshn9515 in #26
• DEP: Update dependency gdown to >=6.2.0,<6.3.0 by @renovate[bot] in #27
• DEP: Update dependency wandb to >=0.30.0,<0.31.0 by @renovate[bot] in #28
• DEP: Update dependency accelerate to >=1.15.0,<1.16.0 by @renovate[bot] in #29
• DEP: Update dependency transformers to >=5.17.0,<5.18.0 by @renovate[bot] in #30
• FEA: Add ch12.3-profiling and corresponding figures by @jshn9515 in #31
• MNT: Move eval mode to cell level and add xFormers dependency by @jshn9515 in #32
• FEA: Add ch12.8-triton and corresponding figures by @jshn9515 in #33
• FEA: Add ch12.6-activation-checkpointing and corresponding figures by @jshn9515 in #34
• FEA: Enhance Netlify deployment workflow with GitHub deployment status by @jshn9515 in #35
• DEP: Update dependency kernels to >=0.17.0,<0.18.0 by @renovate[bot] in #37
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in #38
• FEA: Add ch12.9-distributed-training and corresponding figures by @jshn9515 in #39
• DEP: Update dependency gdown to >=6.3.0,<6.4.0 by @renovate[bot] in #40
• FEA: Add ch12.2-roofline and corresponding figures by @jshn9515 in #41
• DEP: Update dependency gdown to >=6.4.0,<6.5.0 by @renovate[bot] in #43
• FEA: Add ch12.10-large-model-checkpoint and corresponding figures by @jshn9515 in #44
• [DNEP 2] Narrow the Project Scope to Deep Learning Fundamentals and LLMs by @jshn9515 in #46
• FEA: Add ch19.1-from-vllm-to-sglang and corresponding figures by @jshn9515 in #47
• [zh] FEA: Add exercises for chapters 1-4 by @xcherry9988-code in #48
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in #50
• [zh] FEA: Add exercises for chapters 5-6 by @Unicornlyy in #52
• [zh] FEA: Add exercises for chapter 9 by @xcherry9988-code in #53
• [zh] FEA: Add exercises for chapters 11-12 by @Unicornlyy in #58
• [pre-commit.ci] pre-commit autoupdate by @pre-commit-ci[bot] in #59
• DEP: Update dependency torchcodec to >=0.17.0,<0.18.0 by @renovate[bot] in #61
• DEP: Update dependency transformers to >=5.18.0,<5.19.0 by @renovate[bot] in #62

New Contributors

• @pre-commit-ci[bot] made their first contribution in #38
• @xcherry9988-code made their first contribution in #48
• @Unicornlyy made their first contribution in #52

Note

This project continues to be maintained in both Chinese and English through a Quarto-based structure, as an open and continuously growing collection of deep learning study notes.

Full Changelog: v2026.08.24...v2026.10.02