The Curious Engineer

All blogs / AI / ML

Sebastian Raschka, PhD

sebastianraschka.com

Homepage of Sebastian Raschka with recent AI and LLM articles, books, talks, research links, and project resources.

AI / MLPython

’15’20’25
Posts
157 from 3 Nov 2013 to 24 Oct 2026
Posting
weekly (54 in the last year)
Kind
personal blog
Feed
https://sebastianraschka.com/rss_feed.xml

2026 48

  1. Building Reasoning Models from the Ground Up: A Full Video Course
  2. Language Models for Text Classification: From Bag-of-Words to Jev
  3. Focusing on Post-Training
  4. MiMo-V2.6 Pro Architecture and Training Notes
  5. It's Easy to Dismiss Jev as Just a Classifier
  6. Pacing != Pacing Development
  7. AI Reasoning Models Course on LinkedIn Learning
  8. GPT-6 Astra, Looped Transformers, and Hidden Reasoning
  9. OpenAI Astra and Looped Transformers
  10. Reasoning Models From Scratch: Code Setup
  11. Two Live Book Club Q&As on September 3
  12. GLM-5.3-Flash Architecture Notes
  13. How Claude Watermarks AI-Generated Text
  14. Building an AI Text Detector From Scratch
  15. How Claude's Text Watermarking Works
  16. Build a Reasoning Model From Scratch Is Now on Amazon
  17. Muse Glimmer 30B Architecture Notes
  18. LLMs From Scratch Reaches 100,000 GitHub Stars
  19. Kimi K3 Architecture Notes
  20. A Few Notable Open-Weight Models This Week
  21. Correction for Listing 6.5 in Build a Reasoning Model From Scratch
  22. Controlling Reasoning Effort in LLMs
  23. Inkling: A New Open-Weight 975B MoE with a Few Surprises
  24. 200,000 Subscribers
  25. GPT 5.6 Has 72 Possible Configurations. What's A Good Default?
  26. Build a Reasoning Model From Scratch Is Out
  27. Using Local Coding Agents
  28. Using Local Coding Agents
  29. Local Open-Weight LLMs in Coding Harnesses
  30. GLM-5.2 and IndexShare for Long-Context Sparse Attention
  31. VibeThinker-3B and the Strength of Post-Training
  32. North Mini Code and Agentic Coding Benchmarks
  33. LLM Research Papers: The 2026 List (January to May)
  34. Nemotron 3 Ultra and Latent MoE Scaling
  35. MiniMax M2 Technical Report Notes
  36. DeepSeek Sparse Attention From Scratch
  37. Recent Developments in LLM Architectures: KV Sharing, mHC, and Compressed Attention
  38. Implementing LLM Architectures From Scratch
  39. My Workflow for Understanding LLM Architectures
  40. Components of A Coding Agent
  41. Gemma 4 Architecture and Benchmark Notes
  42. LLM Architecture Gallery Diff Tool
  43. A Visual Guide to Attention Variants in Modern LLMs
  44. Inside the LLM Architecture Gallery
  45. Nemotron 3 Super Throughput Notes
  46. A Dream of Spring for Open-Weight LLMs: 10 Architectures from Jan-Feb 2026
  47. State of AI 2026 with Sebastian Raschka, Nathan Lambert, and Lex Fridman
  48. Categories of Inference-Time Scaling for Improved LLM Reasoning

2025 20

  1. The State Of LLMs 2025: Progress, Problems, and Predictions
  2. LLM Research Papers: The 2025 List (July to December)
  3. From Random Forests to RLVR: A Short History of ML/AI Hello Worlds
  4. From DeepSeek V3 to V3.2: Architecture, Sparse Attention, and RL Updates
  5. Recommendations for Getting the Most Out of a Technical Book
  6. Beyond Standard LLMs
  7. DGX Spark and Mac Mini for Local PyTorch Development
  8. Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)
  9. Understanding and Implementing Qwen3 From Scratch
  10. From GPT-2 to gpt-oss: Analyzing the Architectural Advances
  11. The Big LLM Architecture Comparison
  12. LLM Research Papers: The 2025 List (January to June)
  13. Understanding and Coding the KV Cache in LLMs from Scratch
  14. Coding LLMs from the Ground Up: A Complete Course
  15. The State of Reinforcement Learning for LLM Reasoning
  16. First Look at Reasoning From Scratch: Chapter 1
  17. Inference-Time Compute Scaling Methods to Improve Reasoning Models
  18. Understanding Reasoning LLMs
  19. Noteworthy LLM Research Papers of 2024
  20. Implementing A Byte Pair Encoding (BPE) Tokenizer From Scratch

2024 13

  1. LLM Research Papers: The 2024 List
  2. Understanding Multimodal LLMs
  3. Building A GPT-Style LLM Classifier From Scratch
  4. Building LLMs from the Ground Up: A 3-hour Coding Workshop
  5. New LLM Pre-training and Post-training Paradigms
  6. Instruction Pretraining LLMs
  7. Developing an LLM: Building, Training, Finetuning
  8. LLM Research Insights: Instruction Masking and New LoRA Finetuning Experiments?
  9. How Good Are the Latest Open LLMs? And Is DPO Better Than PPO?
  10. Using and Finetuning Pretrained Transformers
  11. Tips for LLM Pretraining and Evaluating Reward Models
  12. Research Papers in February 2024
  13. Improving LoRA: Implementing Weight-Decomposed Low-Rank Adaptation (DoRA) from Scratch

2023 19

  1. Optimizing LLMs From a Dataset Perspective
  2. The NeurIPS 2023 LLM Efficiency Challenge Starter Guide
  3. Optimizing Memory Usage for Training LLMs and Vision Transformers in PyTorch
  4. Finetuning Falcon LLMs More Efficiently With LoRA and Adapters
  5. Accelerating Large Language Models with Mixed-Precision Techniques
  6. Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA)
  7. Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters
  8. Finetuning Large Language Models On A Single GPU Using Gradient Accumulation
  9. Keeping Up With AI Research And News
  10. Some Techniques To Make Your PyTorch Models Train (Much) Faster
  11. Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch
  12. Understanding and Coding Self-Attention, Multi-Head Attention, Causal Attention, and Cross-Attention in LLMs
  13. Understanding Large Language Models -- A Transformative Reading List
  14. What Are the Different Approaches for Detecting Content Generated by LLMs Such As ChatGPT? And How Do They Work and Differ?
  15. Comparing Different Automatic Image Augmentation Methods in PyTorch
  16. Curated Resources and Trustworthy Experts: The Key Ingredients for Finding Accurate Answers to Technical Questions in the Future
  17. Training an XGBoost Classifier Using Cloud GPUs Without Worrying About Infrastructure
  18. Open Source Highlights 2022 for Machine Learning & AI
  19. Influential Machine Learning Papers Of 2022

2022 11

  1. Ahead Of AI, And What's Next?
  2. A Short Chronology Of Deep Learning For Tabular Data
  3. No, We Don't Have to Choose Batch Sizes As Powers Of 2
  4. Sharing Deep Learning Research Models with Lightning Part 2: Leveraging the Cloud
  5. Sharing Deep Learning Research Models with Lightning Part 1: Building A Super Resolution App
  6. Taking Datasets, DataLoaders, and PyTorch’s New DataPipes for a Spin
  7. Running PyTorch on the M1 GPU
  8. Creating Confidence Intervals for Machine Learning Classifiers
  9. Losses Learned
  10. TorchMetrics
  11. Machine Learning with PyTorch and Scikit-Learn

2021 5

  1. Introduction to Machine Learning
  2. Introduction to Deep Learning
  3. Datasets for Machine Learning and Deep Learning
  4. Book Review: Deep Learning With PyTorch
  5. How I Keep My Projects Organized

2020 4

  1. Scientific Computing in Python: Introduction to NumPy and Matplotlib
  2. Interpretable Machine Learning
  3. Chapter 1: Introduction to Machine Learning and Deep Learning
  4. Book Review: Architects of Intelligence by Martin Ford

2019 2

  1. What's New in the 3rd Edition
  2. My First Year at UW-Madison and a Gallery of Awesome Student Projects

2018 2

  1. Model evaluation, model selection, and algorithm selection in machine learning
  2. Generating Gender-Neutral Face Images with Semi-Adversarial Neural Networks to Enhance Privacy

2016 3

  1. Model evaluation, model selection, and algorithm selection in machine learning
  2. Model evaluation, model selection, and algorithm selection in machine learning
  3. Model evaluation, model selection, and algorithm selection in machine learning

2015 5

  1. Writing 'Python Machine Learning'
  2. Python, Machine Learning, and Language Wars
  3. Single-Layer Neural Networks and Gradient Descent
  4. Principal Component Analysis
  5. Implementing a Weighted Majority Rule Ensemble Classifier

2014 22

  1. MusicMood
  2. Turn Your Twitter Timeline into a Word Cloud
  3. Naive Bayes and Text Classification
  4. Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA
  5. Predictive modeling, supervised machine learning, and pattern classification
  6. Linear Discriminant Analysis
  7. Dixon's Q test for outlier identification
  8. About Feature Scaling and Normalization
  9. Entry Point Data
  10. Molecular docking, estimating free energies of binding, and AutoDock's semi-empirical force field
  11. An introduction to parallel programming using Python's multiprocessing module
  12. Kernel density estimation via the Parzen-Rosenblatt window method
  13. Numeric matrix manipulation
  14. The key differences between Python 2.7.x and Python 3.x with examples
  15. 5 simple steps for converting Markdown documents into HTML and adding Python syntax highlighting
  16. Creating a table of contents with internal links in IPython Notebooks and Markdown documents
  17. A Beginner's Guide to Python's Namespaces, Scope Resolution, and the LEGB Rule
  18. Diving deep into Python
  19. Implementing a Principal Component Analysis (PCA)
  20. Installing Scientific Packages for Python3 on MacOS 10.9 Mavericks
  21. A thorough guide to SQLite database operations in Python
  22. Using OpenEye software for substructure alignments

2013 3

  1. Unit testing in Python
  2. A short tutorial for decent heat maps in R
  3. 2013 sqlite database