{"id":13599464,"url":"https://github.com/amitness/learning","last_synced_at":"2025-05-13T17:05:19.626Z","repository":{"id":37412746,"uuid":"112103066","full_name":"amitness/learning","owner":"amitness","description":"A log of things I'm 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learning\n\nA running log of things I'm learning to build strong core software engineering skills while also expanding my knowledge of [adjacent technologies](http://www.effectiveengineer.com/blog/master-adjacent-disciplines) a little bit [everyday](https://jamesclear.com/continuous-improvement).\n\n**Updated**: Once a month | **Current** **Focus**: Generative AI\n\n## Core Skills\n\n\u003e Generic skills that are transferrable to any sort of software work I do\n\n### Python Programming\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Writing Efficient Python Code](https://www.datacamp.com/courses/writing-efficient-python-code)|✅|\n|[Datacamp: Writing Functions in Python](https://www.datacamp.com/courses/writing-functions-in-python)|✅|\n|[Datacamp: Object-Oriented Programming in Python](https://www.datacamp.com/courses/object-oriented-programming-in-python)|✅|\n|[Datacamp: Intermediate Object-Oriented Programming in Python](https://www.datacamp.com/courses/intermediate-object-oriented-programming-in-python)|✅|\n|[Datacamp: Importing Data in Python (Part 1)](https://www.datacamp.com/courses/importing-data-in-python-part-1)|✅|\n|[Datacamp: Importing Data in Python (Part 2)](https://www.datacamp.com/courses/importing-data-in-python-part-2)|✅|\n|[Datacamp: Intermediate Python for Data Science](https://www.datacamp.com/courses/intermediate-python-for-data-science)|✅|\n|[Datacamp: Python Data Science Toolbox (Part 1)](https://www.datacamp.com/courses/python-data-science-toolbox-part-1)|✅|\n|[Datacamp: Python Data Science Toolbox (Part 2)](https://www.datacamp.com/courses/python-data-science-toolbox-part-2)|✅|\n|[Datacamp: Developing Python Packages](https://www.datacamp.com/courses/developing-python-packages)|✅|\n|[Datacamp: Conda Essentials](https://www.datacamp.com/courses/conda-essentials)|✅|\n|[Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit](https://www.youtube.com/watch?v=WkUBx3g2QfQ\u0026feature=youtu.be)|✅|\n|[Datacamp: Working with Dates and Times in Python](https://www.datacamp.com/courses/working-with-dates-and-times-in-python)|✅|\n|[Datacamp: Command Line Automation in Python](https://www.datacamp.com/courses/command-line-automation-in-python)|⬜|\n|[Book: Python 201](https://leanpub.com/python201)|⬜|\n|[Book: Writing Idiomatic Python 3](https://www.amazon.com/Writing-Idiomatic-Python-Jeff-Knupp-ebook/dp/B00B5VXMRG)|⬜|\n|[Article: Python's many command-line utilities](https://www.pythonmorsels.com/cli-tools/)|⬜|\n|[Article: A Programmer’s Introduction to Unicode](https://www.reedbeta.com/blog/programmers-intro-to-unicode/)|⬜|\n|[Article: Exposing string types to maximize user happiness](https://stephantul.github.io/python/typing/2025/03/07/externalized-types/)|✅|\n\n### Testing \u0026 Profiling\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Unit Testing for Data Science in Python](https://www.datacamp.com/courses/unit-testing-for-data-science-in-python)|✅|\n|[Book: Test Driven Development with Python](http://chimera.labs.oreilly.com/books/1234000000754/index.html)|⬜|\n|[Article: Introduction to Memory Profiling in Python](https://www.datacamp.com/tutorial/memory-profiling-python)|✅|\n|[Article: Profiling Python code with memory_profiler](https://www.wrighters.io/profiling-python-code-with-memory_profiler/)|✅|\n|[Article: How to Use \"memory_profiler\" to Profile Memory Usage by Python Code?](https://coderzcolumn.com/tutorials/python/how-to-profile-memory-usage-in-python-using-memory-profiler)|✅|\n|[Youtube: Debug Python inside Docker using debugpy and VSCode](https://www.youtube.com/watch?v=ywfsLKRLmf4)|✅|\n\n\n### Data Structures and Algorithms\n\n|Resource|Progress|\n|---|---|\n|[Book: Grokking Algorithms](https://www.manning.com/books/grokking-algorithms)|✅|\n|[Book: The Tech Resume Inside Out](https://thetechresume.com)|✅|\n|[Neetcode: Algorithms and Data Structures for Beginners](https://neetcode.io/courses/dsa-for-beginners/0)|✅|\n|[Udacity: Intro to Data Structures and Algorithms](https://www.udacity.com/course/technical-interview--ud513)|✅|\n\n\n### Linux \u0026 Command Line\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Introduction to Shell for Data Science](https://www.datacamp.com/courses/introduction-to-shell-for-data-science)|✅|\n|[Datacamp: Introduction to Bash Scripting](https://www.datacamp.com/courses/introduction-to-bash-scripting)|✅|\n|[Datacamp: Data Processing in Shell](https://www.datacamp.com/courses/data-processing-in-shell)|✅|\n|[MIT: The Missing Semester](https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J)|✅|\n|[Udacity: Linux Command Line Basics](https://www.udacity.com/course/linux-command-line-basics--ud595)|✅|\n|[Udacity: Shell Workshop](https://www.udacity.com/course/shell-workshop--ud206)|✅|\n|[Udacity: Configuring Linux Web Servers](https://www.udacity.com/course/configuring-linux-web-servers--ud299)|✅|\n\n### Version Control\n\n|Resource|Progress|\n|---|---|\n|[Udacity: Version Control with Git](https://www.udacity.com/course/version-control-with-git--ud123)|✅|\n|[Datacamp: Introduction to Git for Data Science](https://www.datacamp.com/courses/introduction-to-git-for-data-science)|✅|\n|[Udacity: GitHub \u0026 Collaboration](https://www.udacity.com/course/github-collaboration--ud456)|✅|\n|[Udacity: How to Use Git and GitHub](https://www.udacity.com/course/how-to-use-git-and-github--ud775)|✅|\n|[Youtube: How to Use Git Worktree \\| Checkout Multiple Git Branches at Once](https://youtu.be/s4BTvj1ZVLM)|✅|\n\n### Databases\n\n|Resource|Progress|\n|---|---|\n|[Udacity: Intro to relational database](https://www.udacity.com/course/intro-to-relational-databases--ud197)|✅|\n|[Udacity: Database Systems Concepts \u0026 Design](https://www.udacity.com/course/database-systems-concepts-design--ud150)|⬜|\n|[Datacamp: Database Design](https://www.datacamp.com/courses/database-design)|⬜|\n|[Datacamp: Introduction to Databases in Python](https://www.datacamp.com/courses/introduction-to-relational-databases-in-python)|⬜|\n|[Datacamp: Intro to SQL for Data Science](https://www.datacamp.com/courses/intro-to-sql-for-data-science)|✅|\n|[Datacamp: Intermediate SQL](https://www.datacamp.com/courses/intermediate-sql)|⬜|\n|[Datacamp: Joining Data in PostgreSQL](https://www.datacamp.com/courses/joining-data-in-postgresql)|⬜|\n|[Udacity: SQL for Data Analysis](https://www.udacity.com/course/sql-for-data-analysis--ud198)|⬜|\n|[Datacamp: Exploratory Data Analysis in SQL](https://www.datacamp.com/courses/sql-for-exploratory-data-analysis)|⬜|\n|[Datacamp: Applying SQL to Real-World Problems](https://www.datacamp.com/courses/applying-sql-to-real-world-problems)|⬜|\n|[Datacamp: Analyzing Business Data in SQL](https://www.datacamp.com/courses/analyzing-business-data-in-sql)|⬜|\n|[Datacamp: Reporting in SQL](https://www.datacamp.com/courses/reporting-in-sql)|⬜|\n|[Datacamp: Data-Driven Decision Making in SQL](https://www.datacamp.com/courses/data-driven-decision-making-with-sql)|⬜|\n|[Datacamp: NoSQL Concepts](https://www.datacamp.com/courses/nosql-concepts)|⬜|\n|[Datacamp: Introduction to MongoDB in Python](https://www.datacamp.com/courses/introduction-to-using-mongodb-for-data-science-with-python)|⬜|\n\n### Backend Engineering\n\n|Resource|Progress|\n|---|---|\n|[Udacity: Authentication \u0026 Authorization: OAuth](https://www.udacity.com/course/authentication-authorization-oauth--ud330)|⬜|\n|[Udacity: HTTP \u0026 Web Servers](https://www.udacity.com/course/http-web-servers--ud303)|⬜|\n|[Udacity: Client-Server Communication](https://www.udacity.com/course/client-server-communication--ud897)|⬜|\n|[Udacity: Designing RESTful APIs](https://www.udacity.com/course/designing-restful-apis--ud388)|⬜|\n|[Datacamp: Introduction to APIs in Python](https://www.datacamp.com/courses/introduction-to-apis-in-python)|⬜|\n|[Udacity: Networking for Web Developers](https://www.udacity.com/course/networking-for-web-developers--ud256)|⬜|\n\n### Production System Design\n\n|Resource|Progress|\n|---|---|\n|[Book: Designing Machine Learning Systems](https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/)|✅|\n|[Neetcode: System Design for Beginners](https://neetcode.io/courses/system-design-for-beginners/0)|✅|\n|[Neetcode: System Design Interview](https://neetcode.io/courses/system-design-interview)|✅|\n|[Datacamp: Customer Analytics \u0026 A/B Testing in Python](https://www.datacamp.com/courses/customer-analytics-ab-testing-in-python)|✅|\n|[Datacamp: A/B Testing in Python](https://www.datacamp.com/courses/ab-testing-in-python)|⬜|\n|[Udacity: A/B Testing](https://www.udacity.com/course/ab-testing--ud257)|⬜|\n|[Datacamp: MLOps Concepts](https://www.datacamp.com/courses/mlops-concepts)|✅|\n|[Datacamp: Machine Learning Monitoring Concepts](https://www.datacamp.com/courses/machine-learning-monitoring-concepts)|✅|\n\n\n### Maths\n\t\n|Resource|Progress|\n|---|---|\n|[Datacamp: Foundations of Probability in Python](https://www.datacamp.com/courses/foundations-of-probability-in-python)|✅|\n|[Datacamp: Introduction to Statistics](https://www.datacamp.com/courses/introduction-to-statistics)|✅|\n|[Datacamp: Introduction to Statistics in Python](https://www.datacamp.com/courses/introduction-to-statistics-in-python)|✅|\n|[Datacamp: Hypothesis Testing in Python](https://www.datacamp.com/courses/hypothesis-testing-in-python)|✅|\n|[Datacamp: Statistical Thinking in Python (Part 1)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-1)|✅|\n|[Datacamp: Statistical Thinking in Python (Part 2)](https://www.datacamp.com/courses/statistical-thinking-in-python-part-2)|✅|\n|[Datacamp: Experimental Design in Python](https://datacamp.com/courses/experimental-design-in-python)|✅|\n|[Datacamp: Practicing Statistics Interview Questions in Python](https://www.datacamp.com/courses/practicing-statistics-interview-questions-in-python)|⬜|\n|[edX: Essential Statistics for Data Analysis using Excel](https://www.edx.org/course/essential-statistics-data-analysis-using-microsoft-dat222x-1)|✅|\n|[Udacity: Intro to Inferential Statistics](https://www.udacity.com/course/intro-to-inferential-statistics--ud201)|✅|\n|[MIT 18.06 Linear Algebra, Spring 2005](https://www.youtube.com/playlist?list=PLE7DDD91010BC51F8)|✅|\n|[Udacity: Eigenvectors and Eigenvalues](https://www.udacity.com/course/eigenvectors-and-eigenvalues--ud104)|✅|\n|[Udacity: Linear Algebra Refresher](https://www.udacity.com/course/linear-algebra-refresher-course--ud953)|⬜|\n|[Youtube: Essence of linear algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)|⬜|\n\n## Specialization\n\u003chr\u003e\n\n### Traditional Machine Learning\n\n|Resource|Progress|\n|---|---|\n|[Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)|⬜|\n|[Book: A Machine Learning Primer](https://www.confetti.ai/assets/ml-primer/ml_primer.pdf)|✅|\n|[Book: Grokking Machine Learning](https://www.manning.com/books/grokking-machine-learning)|✅|\n|[Book: The StatQuest Illustrated Guide To Machine Learning](https://www.amazon.com/StatQuest-Illustrated-Guide-Machine-Learning/dp/B0BLM4TLPY)|✅|\n|[Datacamp: Ensemble Methods in Python](https://www.datacamp.com/courses/ensemble-methods-in-python)|✅|\n|[Datacamp: Extreme Gradient Boosting with XGBoost](https://www.datacamp.com/courses/extreme-gradient-boosting-with-xgboost)|⬜|\n|[Datacamp: Clustering Methods with SciPy](https://www.datacamp.com/courses/clustering-methods-with-scipy)|✅|\n|[Datacamp: Unsupervised Learning in Python](https://www.datacamp.com/courses/unsupervised-learning-in-python)|✅|\n|[Udacity: Segmentation and Clustering](https://www.udacity.com/course/segmentation-and-clustering--ud981)|✅|\n|[Datacamp: Intro to Python for Data Science](https://www.datacamp.com/courses/intro-to-python-for-data-science)|✅|\n|[edX: Implementing Predictive Analytics with Spark in Azure HDInsight](https://www.edx.org/course/implementing-predictive-analytics-spark-microsoft-dat202-3x-2)|✅|\n|[Datacamp: Supervised Learning with scikit-learn](https://www.datacamp.com/courses/supervised-learning-with-scikit-learn)|✅|\n|[Datacamp: Machine Learning with Tree-Based Models in Python](https://www.datacamp.com/courses/machine-learning-with-tree-based-models-in-python)|✅|\n|[Datacamp: Linear Classifiers in Python](https://www.datacamp.com/courses/linear-classifiers-in-python)|✅|\n|[Datacamp: Model Validation in Python](https://www.datacamp.com/courses/model-validation-in-python)|✅|\n|[Datacamp: Hyperparameter Tuning in Python](https://www.datacamp.com/courses/hyperparameter-tuning-in-python)|✅|\n|[Datacamp: HR Analytics in Python: Predicting Employee Churn](https://www.datacamp.com/courses/hr-analytics-in-python-predicting-employee-churn)|✅|\n|[Datacamp: Predicting Customer Churn in Python](https://www.datacamp.com/courses/predicting-customer-churn-in-python)|✅|\n|[Datacamp: Dimensionality Reduction in Python](https://www.datacamp.com/courses/dimensionality-reduction-in-python)|✅|\n|[Datacamp: Preprocessing for Machine Learning in Python](https://www.datacamp.com/courses/preprocessing-for-machine-learning-in-python)|✅|\n|[Datacamp: Data Types for Data Science](https://www.datacamp.com/courses/data-types-for-data-science)|✅|\n|[Datacamp: Cleaning Data in Python](https://www.datacamp.com/courses/cleaning-data-in-python)|✅|\n|[Datacamp: Feature Engineering for Machine Learning in Python](https://www.datacamp.com/courses/feature-engineering-for-machine-learning-in-python)|✅|\n|[Datacamp: Predicting CTR with Machine Learning in Python](https://www.datacamp.com/courses/predicting-ctr-with-machine-learning-in-python)|✅|\n|[Datacamp: Intro to Financial Concepts using Python](https://www.datacamp.com/courses/intro-to-financial-concepts-using-python)|✅|\n|[Datacamp: Fraud Detection in Python](https://www.datacamp.com/courses/fraud-detection-in-python)|✅|\n\n\n### Deep Learning\n\n|Resource|Progress|\n|---|---|\n|[Article: An overview of gradient descent optimization algorithms](https://www.ruder.io/optimizing-gradient-descent)|✅|\n|[Book: Make Your Own Neural Network](https://www.amazon.com/Make-Your-Own-Neural-Network/dp/1530826608)|✅|\n|[Fast.ai: Practical Deep Learning for Coder (Part 1)](https://course.fast.ai/)|✅|\n|[Fast.ai: Practical Deep Learning for Coder (Part 2)](https://course.fast.ai/Lessons/part2.html) `9, 13,14,17,18(48:10),`|⬜|\n|[Datacamp: Convolutional Neural Networks for Image Processing](https://www.datacamp.com/courses/convolutional-neural-networks-for-image-processing)|✅|\n|[Karpathy: Neural Networks: Zero to Hero](https://github.com/karpathy/nn-zero-to-hero/)|✅|\n|[Article: Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming](https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79)|⬜|\n|[Article: Things that confused me about cross-entropy](https://chris-said.io/2020/12/26/two-things-that-confused-me-about-cross-entropy/)|✅|\n\n### Natural Language Processing\n\n|Resource|Progress|\n|---|---|\n|[Book: Natural Language Processing with Transformers](https://transformersbook.com/)|✅|\n|[Stanford CS224U: Natural Language Understanding \\| Spring 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20)|✅|\n|[Stanford CS224N: Stanford CS224N: NLP with Deep Learning \\| Winter 2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z)|✅|\n|[CMU: Low-resource NLP Bootcamp 2020](https://www.youtube.com/playlist?list=PL8PYTP1V4I8A1CpCzURXAUa6H4HO7PF2c)|✅|\n|[CMU Multilingual NLP 2020](http://demo.clab.cs.cmu.edu/11737fa20/)|✅|\n|[Datacamp: Feature Engineering for NLP in Python](https://www.datacamp.com/courses/feature-engineering-for-nlp-in-python)|✅|\n|[Datacamp: Natural Language Processing Fundamentals in Python](https://www.datacamp.com/courses/natural-language-processing-fundamentals-in-python)|✅|\n|[Datacamp: Regular Expressions in Python](https://www.datacamp.com/courses/regular-expressions-in-python)|✅|\n|[Datacamp: RNN for Language Modeling](https://www.datacamp.com/courses/recurrent-neural-networks-for-language-modeling-in-python)|✅|\n|[Datacamp: Natural Language Generation in Python](https://www.datacamp.com/courses/natural-language-generation-in-python)|✅|\n|[Datacamp: Building Chatbots in Python](https://www.datacamp.com/courses/building-chatbots-in-python)|✅|\n|[Datacamp: Sentiment Analysis in Python](https://www.datacamp.com/courses/sentiment-analysis-in-python)|✅|\n|[Datacamp: Machine Translation in Python](https://www.datacamp.com/courses/machine-translation-in-python)|✅|\n|[Article: The Unreasonable Effectiveness of Collocations](https://opensourceconnections.com/blog/2019/05/16/unreasonable-effectiveness-of-collocations/)|⬜|\n|[Article: FuzzyWuzzy: Fuzzy String Matching in Python](https://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/#)|✅|\n|[Article: Mamba Explained](https://thegradient.pub/mamba-explained/)|⬜|\n|[Article: A Visual Guide to Mamba and State Space Models](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mamba-and-state)|⬜|\n|[Article: Transformers: Origins](https://mark-riedl.medium.com/transformers-origins-1db4bdfcb3d1)|⬜|\n\n### Generative AI\n\u003chr\u003e\n\n#### LLM Theory\n\n|Resource|Progress|\n|---|---|\n|[Book: Hands-On Large Language Models: Language Understanding and Generation](https://www.amazon.com/Hands-Large-Language-Models-Understanding/dp/1098150961)|✅|\n|[Book: AI Engineering: Building Applications with Foundation Models](https://www.amazon.com/AI-Engineering-Building-Applications-Foundation/dp/1098166302)|✅|\n|[Book: Designing Large Language Model Applications](https://www.oreilly.com/library/view/designing-large-language/9781098150495/)|⬜|\n|[Book: Large Language Models: A Deep Dive: Bridging Theory and Practice](https://www.amazon.com/Large-Language-Models-Bridging-Practice/dp/3031656466)|⬜|\n|[Book: A Little Bit of Reinforcement Learning from Human Feedback](https://rlhfbook.com/)|✅|\n|[Article: From Digits to Decisions: How Tokenization Impacts Arithmetic in LLMs](https://huggingface.co/spaces/huggingface/number-tokenization-blog)|⬜|\n|[Article: SolidGoldMagikarp (plus, prompt generation)](https://www.lesswrong.com/posts/aPeJE8bSo6rAFoLqg/solidgoldmagikarp-plus-prompt-generation)|⬜|\n|[Article: Sampling for Text Generation](https://huyenchip.com/2024/01/16/sampling.html)|⬜|\n|[Article: You could have designed state of the art Positional Encoding](https://fleetwood.dev/posts/you-could-have-designed-SOTA-positional-encoding)|⬜|\n|[Article: Scaling test-time compute - a Hugging Face Space by HuggingFaceH4](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute)|⬜|\n|[Article: DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs](https://www.interconnects.ai/p/deepseek-r1-recipe-for-o1)|✅|\n|[Article: The Illustrated DeepSeek-R1](https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1)|✅|\n|[Article: A Visual Guide to Reasoning LLMs](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-reasoning-llms)|⬜|\n|[DeepLearning.AI: Pretraining LLMs](https://www.deeplearning.ai/short-courses/pretraining-llms)|✅|\n|[DeepLearning.AI: Reinforcement Learning from Human Feedback](https://www.deeplearning.ai/short-courses/reinforcement-learning-from-human-feedback)|✅|\n|[Karpathy: Intro to Large Language Models](https://www.youtube.com/watch?v=zjkBMFhNj_g) `1hr`|✅|\n|[Karpathy: Let's build the GPT Tokenizer](https://www.youtube.com/watch?v=zduSFxRajkE) `2hr13m`|✅|\n|[Karpathy: Let's reproduce GPT-2 (124M)](https://www.youtube.com/watch?v=l8pRSuU81PU) `4hr1m`|✅|\n|[Youtube: A Hackers' Guide to Language Models](https://www.youtube.com/watch?v=jkrNMKz9pWU) `1hr30m`|✅|\n|[Karpathy: Deep Dive into LLMs like ChatGPT](https://www.youtube.com/watch?v=7xTGNNLPyMI) `3h31m`|✅|\n|[Youtube: 5 Years of GPTs with Finbarr Timbers](https://www.youtube.com/watch?v=YA0pzBYAV2Q\u0026list=PLKlhhkvvU8-YxMP9hjEYJTJDCaGszrJIh\u0026index=8\u0026t=43s) `55m`|✅|\n|[Youtube: Stanford CS229 I Machine Learning I Building Large Language Models (LLMs)](https://www.youtube.com/watch?v=9vM4p9NN0Ts) `1h44m`|✅|\n|[Youtube: LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU](https://www.youtube.com/watch?v=Mn_9W1nCFLo) `1h10m`|✅|\n|[Youtube: CMU Advanced NLP Fall 2024 (7): Prompting and Complex Reasoning](https://www.youtube.com/watch?v=1Faf1cTe3T8\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=2)|⬜|\n|[Youtube: CMU Advanced NLP Fall 2024 (6): Instruction Tuning](https://www.youtube.com/watch?v=iWcGS0gCL1E\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=3)|⬜|\n|[Youtube: CMU Advanced NLP Fall 2024 (12): Domain Specific Modeling: Code and Math](https://www.youtube.com/watch?v=qHNUVpKO2dc\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=4)|⬜|\n|[Youtube: CMU Advanced NLP Fall 2024 (15): Tool Use and LLM Agent Basics](https://www.youtube.com/watch?v=a3SjRsqV9ZA\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=16)|⬜|\n|[Youtube: CMU Advanced NLP Fall 2024 (14): Ensembling and Mixture of Experts](https://www.youtube.com/watch?v=E4Rg4qTw4xw\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=15)|⬜|\n|[Youtube: A little guide to building Large Language Models in 2024](https://www.youtube.com/watch?v=2-SPH9hIKT8) `1h15m`|✅|\n|[Youtube: How to approach post-training for AI applications](https://www.youtube.com/watch?v=grpc-Wyy-Zg) `22m`|✅|\n|[Youtube: Speculations on Test-Time Scaling (o1) `47m`](https://www.youtube.com/watch?v=6PEJ96k1kiw)|✅|\n|[Youtube: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://youtu.be/XMnxKGVnEUc) `1h19m`|✅|\n|[Youtube: How DeepSeek Changes the LLM Story](https://www.youtube.com/watch?v=0eMzc-WnBfQ)|✅|\n|[Youtube: MIT EI seminar, Hyung Won Chung from OpenAI. \"Don't teach. Incentivize.\"](https://www.youtube.com/watch?v=kYWUEV_e2ss) `35m`|✅|\n|[Youtube: How I use LLMs](https://youtu.be/EWvNQjAaOHw) `2h7m`|✅|\n\n#### Multi-modality\n\n|Resource|Progress|\n|---|---|\n|[Article: Understanding Multimodal LLMs](https://magazine.sebastianraschka.com/p/understanding-multimodal-llms)|⬜|\n|[Article: GPT-4 Vision Alternatives](https://blog.roboflow.com/gpt-4-vision-alternatives/)|⬜|\n|[Article: Computer-Using Agent](https://openai.com/index/computer-using-agent/)|✅|\n|[Youtube: AI Visions Live \\| Merve Noyan \\| Open-source Multimodality](https://www.youtube.com/watch?v=_TlhKHTgWjY) `54m`|✅|\n|[DeepLearning.AI: How Diffusion Models Work](https://www.deeplearning.ai/short-courses/how-diffusion-models-work/)|⬜|\n|[DeepLearning.AI: Prompt Engineering for Vision Models](https://www.deeplearning.ai/short-courses/prompt-engineering-for-vision-models/)|⬜|\n|[DeepLearning.AI: Building Multimodal Search and RAG](https://www.deeplearning.ai/short-courses/building-multimodal-search-and-rag/)|✅|\n|[Pinecone: Embedding Methods for Image Search](https://www.pinecone.io/learn/series/image-search/)|0/8|\n|[Youtube: Lesson 9A 2022 - Stable Diffusion deep dive](https://youtu.be/0_BBRNYInx8)|✅|\n\n\n#### Information Retrieval / RAG\n\n| Resource                                                                                                                                       | Progress |\n| ---------------------------------------------------------------------------------------------------------------------------------------------- | -------- |\n| [Article: Pretrained Transformer Language Models for Search - part 1](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-1/#) | ✅        |\n| [Article: Pretrained Transformer Language Models for Search - part 2](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-2/)  | ✅        |\n| [Article: Pretrained Transformer Language Models for Search - part 3](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-3)   | ✅       |\n| [Article: Pretrained Transformer Language Models for Search - part 4](https://blog.vespa.ai/pretrained-transformer-language-models-for-search-part-4)   | ✅        |\n|[Article: How not to use BERT for Document Ranking](https://bergum.medium.com/how-not-to-use-bert-for-search-ranking-4586716428d9)|✅|\n| [Article: Understanding LanceDB's IVF-PQ index](https://lancedb.github.io/lancedb/concepts/index_ivfpq/)                                                | ✅        |\n| [Article: A little pooling goes a long way for multi-vector representations](https://www.answer.ai/posts/colbert-pooling.html)                          | ✅        |\n|[Article: Levels of Complexity: RAG Applications](https://jxnl.github.io/blog/writing/2024/02/28/levels-of-complexity-rag-applications/)|✅|\n|[Article: Systematically Improving Your RAG](https://jxnl.github.io/blog/writing/2024/05/22/systematically-improving-your-rag/)|✅|\n|[Article: Stop using LGTM@Few as a metric (Better RAG)](https://jxnl.github.io/blog/writing/2024/02/05/when-to-lgtm-at-k/)|✅|\n|[Article: Low-Hanging Fruit for RAG Search](https://jxnl.github.io/blog/writing/2024/05/11/low-hanging-fruit-for-rag-search/)|✅|\n|[Article: What AI Engineers Should Know about Search](https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search)|✅|\n|[Article: Evaluating Chunking Strategies for Retrieval](https://research.trychroma.com/evaluating-chunking)|✅|\n|[Article: Sentence Embeddings. Introduction to Sentence Embeddings](https://osanseviero.github.io/hackerllama/blog/posts/sentence_embeddings/)|✅|\n|[Article: LambdaMART in Depth](https://softwaredoug.com/blog/2022/01/17/lambdamart-in-depth)|⬜|\n|[Article: Guided Generation with Outlines](https://medium.com/canoe-intelligence-technology/guided-generation-with-outlines-c09a0c2ce9eb)|✅|\n|[Article: RAG tricks from the trenches](https://duarteocarmo.com/blog/rag-tricks-from-the-trenches)|⬜|\n|[Article: Retrieval 101](https://isaacflath.com/blog/blog_post?fpath=posts%2F2025-03-17-Retrieval101.ipynb)|⬜|\n| [Course: Fullstack Retrieval](https://community.fullstackretrieval.com/)                                                                        |         ⬜ |\n|[DeepLearning.AI: Building and Evaluating Advanced RAG Applications](https://www.deeplearning.ai/short-courses/building-evaluating-advanced-rag/)|✅|\n|[DeepLearning.AI: Vector Databases: from Embeddings to Applications](https://www.deeplearning.ai/short-courses/vector-databases-embeddings-applications/)|✅|\n|[DeepLearning.AI: Advanced Retrieval for AI with Chroma](https://www.deeplearning.ai/short-courses/advanced-retrieval-for-ai/)|✅|\n|[DeepLearning.AI: Prompt Compression and Query Optimization](https://www.deeplearning.ai/short-courses/prompt-compression-and-query-optimization/)|✅|\n|[DeepLearning.AI: Large Language Models with Semantic Search](https://www.deeplearning.ai/short-courses/large-language-models-semantic-search) `1hr`|✅|\n|[DeepLearning.AI: Building Applications with Vector Databases](https://www.deeplearning.ai/short-courses/building-applications-vector-databases/)|✅|\n|[DeepLearning.AI: Knowledge Graphs for RAG](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/)|⬜|\n|[DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications](https://www.deeplearning.ai/short-courses/preprocessing-unstructured-data-for-llm-applications/)|⬜|\n|[DeepLearning.AI: Embedding Models: From Architecture to Implementation](https://www.deeplearning.ai/short-courses/embedding-models-from-architecture-to-implementation)|✅|\n|[DeepLearning.AI: Retrieval Optimization - From Tokenization to Vector Quantization](https://www.deeplearning.ai/short-courses/retrieval-optimization-from-tokenization-to-vector-quantization/)|✅|\n|[Pinecone: Vector Databases in Production for Busy Engineers](https://www.pinecone.io/learn/series/vector-databases-in-production-for-busy-engineers/)|✅|\n|[Pinecone: Retrieval Augmented Generation](https://www.pinecone.io/learn/series/rag/)|✅|\n|[Pinecone: Faiss: The Missing Manual](https://www.pinecone.io/learn/series/faiss/)|✅|\n|[Pinecone: Natural Language Processing for Semantic Search](https://www.pinecone.io/learn/series/nlp/)|0/13|\n|[Youtube: Systematically improving RAG applications](https://youtu.be/RrDBV6odPKo?list=PLgIaq8VgndJvXkDSeReTl2u4rQMShkZ6V)|✅|\n|[Youtube: Back to Basics for RAG w/ Jo Bergum](https://www.youtube.com/watch?v=nc0BupOkrhI\u0026list=PLgIaq8VgndJvXkDSeReTl2u4rQMShkZ6V\u0026index=2)|✅|\n|[Youtube: Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié)](https://www.youtube.com/watch?v=0nA5QG3087g\u0026t=1287s)|✅|\n|[Youtube: RAG From Scratch](https://www.youtube.com/playlist?list=PLfaIDFEXuae2LXbO1_PKyVJiQ23ZztA0x) `14/14`|✅|\n|[Youtube: CMU Advanced NLP Fall 2024 (10): Retrieval and RAG](https://www.youtube.com/watch?v=KfQaYk4k9eM\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=6) `1h17m`|✅|\n|[Guidance: Token Healing](https://github.com/guidance-ai/guidance/blob/main/notebooks/tutorials/token_healing.ipynb)|⬜|\n|[Youtube: What You See Is What You Search: Vision Language Models for PDF Retrieval [Jo Bergum]](https://youtu.be/qrbQUU4TrLM)|✅|\n\n#### Agentic Pattern\n\n|Resource|Progress|\n|---|---|\n|[Article: Tool Invocation - Demonstrating the Marvel of GPT's Flexibility](https://blog.jnbrymn.com/2024/01/30/the-marvel-of-GPT-generality.html)|⬜|\n|[Article: Introducing smolagents, a simple library to build agents](https://huggingface.co/blog/smolagents)|⬜|\n|[Article: What Problem Does The Model Context Protocol Solve?](https://www.aihero.dev/what-problem-does-model-context-protocol-solve)|✅|\n|[Anthropic: Building effective agents](https://www.anthropic.com/research/building-effective-agents)|✅|\n|[Anthropic: Building Effective Agents Cookbook](https://github.com/anthropics/anthropic-cookbook/tree/main/patterns/agents)|✅|\n|[OpenAI: Assistants \u0026 Agents Build Hour](https://vimeo.com/showcase/11333741/video/990334325)|✅|\n|[OpenAI: Function Calling Build Hour](https://vimeo.com/showcase/11333741/video/952127114)|✅|\n|[DeepLearning.AI: Functions, Tools and Agents with LangChain](https://www.deeplearning.ai/short-courses/functions-tools-agents-langchain/)|⬜|\n|[DeepLearning.AI: Building Agentic RAG with LlamaIndex](https://www.deeplearning.ai/short-courses/building-agentic-rag-with-llamaindex/)|✅|\n|[DeepLearning.AI: Multi AI Agent Systems with crewAI](https://www.deeplearning.ai/short-courses/multi-ai-agent-systems-with-crewai/)|✅|\n|[DeepLearning.AI: Building Towards Computer Use with Anthropic](https://www.deeplearning.ai/short-courses/building-towards-computer-use-with-anthropic/)|✅|\n|[DeepLearning.AI: Practical Multi AI Agents and Advanced Use Cases with crewAI](https://www.deeplearning.ai/short-courses/practical-multi-ai-agents-and-advanced-use-cases-with-crewai/)|⬜|\n|[DeepLearning.AI: LLMs as Operating Systems: Agent Memory](https://www.deeplearning.ai/short-courses/llms-as-operating-systems-agent-memory/)|✅|\n|[DeepLearning.AI: Serverless Agentic Workflows with Amazon Bedrock](https://www.deeplearning.ai/short-courses/serverless-agentic-workflows-with-amazon-bedrock/)|⬜|\n|[DeepLearning.AI: AI Agentic Design Patterns with AutoGen](https://www.deeplearning.ai/short-courses/ai-agentic-design-patterns-with-autogen/)|⬜|\n|[DeepLearning.AI: AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)|⬜|\n|[DeepLearning.AI: Building Your Own Database Agent](https://www.deeplearning.ai/short-courses/building-your-own-database-agent/)|⬜|\n|[DeepLearning.AI: Function-Calling and Data Extraction with LLMs](https://www.deeplearning.ai/short-courses/function-calling-and-data-extraction-with-llms/) `59m`|✅|\n|[DeepLearning.AI: Evaluating AI Agents](https://www.deeplearning.ai/short-courses/evaluating-ai-agents/) `2h16m`|✅|\n|[Huggingface: Agents Course](https://huggingface.co/learn/agents-course/unit1/messages-and-special-tokens#base-models-vs-instruct-models)|Unit 1|\n|[Youtube: How to Evaluate Agents: Galileo’s Agentic Evaluations in Action](https://www.youtube.com/watch?v=QvStk5G8BZw)|✅|\n|[Youtube: Agent Response \\| LangSmith Evaluation - Part 24](https://youtu.be/NbQKDfSw3gM?list=PLfaIDFEXuae0um8Fj0V4dHG37fGFU8Q5S)|✅|\n|[Youtube: Single Step \\| LangSmith Evaluation - Part 25](https://youtu.be/AVPflFmRkd4?list=PLfaIDFEXuae0um8Fj0V4dHG37fGFU8Q5S)|✅|\n|[Youtube: Agent Trajectory \\| LangSmith Evaluation - Part 26](https://youtu.be/pvlT056DAHs?list=PLfaIDFEXuae0um8Fj0V4dHG37fGFU8Q5S)|✅|\n|[Youtube: Evaluating Agents and Assistants: The AI Conference](https://www.youtube.com/watch?v=6uXWhmDRcMc)|✅|\n|[Youtube: How to Build, Evaluate, and Iterate on LLM Agents](https://youtu.be/0pnEUAwoDP0)|✅|\n|[Youtube:  Agentic AI: A Progression of Language Model Usage](https://www.youtube.com/watch?v=kJLiOGle3Lw)|⬜|\n\n\n#### Prompt Engineering\n\n|Resource|Progress|\n|---|---|\n|[Article: OpenAI Prompt Engineering](https://platform.openai.com/docs/guides/prompt-engineering)|⬜|\n|[Article: Prompting Fundamentals and How to Apply them Effectively](https://eugeneyan.com/writing/prompting/)|✅|\n|[Article: How I came in first on ARC-AGI-Pub using Sonnet 3.5 with Evolutionary Test-time Compute](https://params.com/@jeremy-berman/arc-agi)|✅|\n|[Anthropic Courses](https://github.com/anthropics/courses)|⬜|\n|[Anthropic: The Claude in Amazon Bedrock Course](https://www.anthropic.com/aws-reinvent-2024/course)|⬜|\n|[Article: Prompt Engineering(Liliang Weng)](https://lilianweng.github.io/posts/2023-03-15-prompt-engineering/)|✅|\n|[Article: Prompt Engineering 201: Advanced methods and toolkits](https://amatria.in/blog/prompt201)|✅|\n|[Article: Optimizing LLMs for accuracy](https://platform.openai.com/docs/guides/optimizing-llm-accuracy)|✅|\n|[Article: Primers • Prompt Engineering](https://aman.ai/primers/ai/prompt-engineering/)|⬜|\n|[Article: Anyscale Endpoints: JSON Mode and Function calling Features](https://www.anyscale.com/blog/anyscale-endpoints-json-mode-and-function-calling-features)|⬜|\n|[Article: Guided text generation with Large Language Models](https://medium.com/productizing-language-models/guided-text-generation-with-large-language-models-d88fc3dcf4c)|⬜|\n|[Book: Prompt Engineering for LLMs](https://www.oreilly.com/library/view/prompt-engineering-for/9781098156145/)|⬜|\n|[DeepLearning.AI: Reasoning with o1](https://www.deeplearning.ai/short-courses/reasoning-with-o1/)|✅|\n|[OpenAI: Reasoning with o1 Build Hour](https://vimeo.com/showcase/11333741/video/1018737829)|✅|\n|[DeepLearning.AI: ChatGPT Prompt Engineering for Developers](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)|⬜|\n|[DeepLearning.AI: Prompt Engineering with Llama 2 \u0026 3](https://www.deeplearning.ai/short-courses/prompt-engineering-with-llama-2/)|⬜|\n|[Wandb: LLM Engineering: Structured Outputs](https://www.wandb.courses/courses/steering-language-models)|⬜|\n|[Series: Prompt injection](https://simonwillison.net/series/prompt-injection/)|⬜|\n|[Youtube: Prompt Engineering Overview](https://www.youtube.com/watch?v=dOxUroR57xs) `1hr4m`|✅|\n|[Youtube: Prompt Engineering Workshop](https://youtu.be/htBTho6oEJA) `1h`|✅|\n\n#### Quantization\n|Resource|Progress|\n|---|---|\n|[Article: Quantization Fundamentals with Hugging Face](https://www.deeplearning.ai/short-courses/quantization-fundamentals-with-hugging-face/)|✅|\n|[DeepLearning.AI: Quantization in Depth](https://www.deeplearning.ai/short-courses/quantization-in-depth/)|⬜|\n|[DeepLearning.AI: Introduction to On-Device AI](https://www.deeplearning.ai/short-courses/introduction-to-on-device-ai/)|⬜|\n|[Article: A Visual Guide to Quantization](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-quantization)|⬜|\n|[Article: QLoRA and 4-bit Quantization](https://mccormickml.com/2024/09/14/qlora-and-4bit-quantization/)|⬜|\n|[Article: Understanding AI/LLM Quantisation Through Interactive Visualisations](https://smcleod.net/2024/07/understanding-ai/llm-quantisation-through-interactive-visualisations/)|⬜|\n|[Youtube: CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning](https://www.youtube.com/watch?v=DvVGkj4zhVU\u0026list=PL8PYTP1V4I8D4BeyjwWczukWq9d8PNyZp\u0026index=5)|⬜|\n\n#### Distributed Training\n\n|Resource|Progress|\n|---|---|\n|[Youtube: Slaying OOMs with PyTorch FSDP and torchao](https://youtu.be/UvRl4ansfCg) `49m`|✅|\n|[Youtube: Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code](https://youtu.be/toUSzwR0EV8) `1h12m`|✅|\n|[Youtube: How DDP works \\|\\| Distributed Data Parallel ](https://youtu.be/bwNtfxEDjGA)|✅|\n|[Youtube: FSDP Explained](https://youtu.be/6pVn6khIgiI)|✅|\n|[Youtube: Lecture 48: The Ultra Scale Playbook](https://youtu.be/1E8GDR8QXKw) `3h3m`|`44:24`|\n|[Youtube: Invited Talk: PyTorch Distributed (DDP, RPC) - By Facebook Research Scientist Shen Li](https://youtu.be/3XUG7cjte2U)|✅|\n|[Youtube: Unit 9 \\| Techniques for Speeding Up Model Training](https://www.youtube.com/playlist?list=PLaMu-SDt_RB403GN5DU7NYVoVmO5Vsgkh)|✅|\n|[Article: A Short Guide to PyTorch DDP](https://blog.hpc.qmul.ac.uk/pytorch-ddp/)|✅|\n|[Article: Scaling Deep Learning with PyTorch: Multi-Node and Multi-GPU Training Explained (with Code)](https://medium.com/@ashraf.kasem.94.0/scaling-deep-learning-with-pytorch-multi-node-and-multi-gpu-training-explained-with-code-ece8f03ea59b)|✅|\n|[Article: Accelerating PyTorch Model Training](https://magazine.sebastianraschka.com/p/accelerating-pytorch-model-training)|✅|\n|[Article: Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow](https://www.uber.com/blog/horovod/)|✅|\n|[Article: Distributed data parallel training in Pytorch](https://yangkky.github.io/2019/07/08/distributed-pytorch-tutorial.html)|✅|\n|[Article: Training on Multiple GPUs](https://d2l.ai/chapter_computational-performance/multiple-gpus.html)|✅|\n\n\n#### Parallel Computing\n\n|Resource|Progress|\n|---|---|\n|[Udacity: Intro to Parallel Programming](https://www.youtube.com/playlist?list=PLAwxTw4SYaPnFKojVQrmyOGFCqHTxfdv2) `458 videos`|299/458|\n|[Book: Programming Massively Parallel Processors: A Hands-on Approach](https://www.amazon.com/Programming-Massively-Parallel-Processors-Hands/dp/0124159923)|Ch. 2|\n|[Youtube: GPU Puzzles: Let's Play](https://youtu.be/K4T-YwsOxrM)|⬜|\n\n#### Inference Optimization\n\n|Resource|Progress|\n|---|---|\n|[Article: How to make LLMs go fast](https://vgel.me/posts/faster-inference/)|⬜|\n|[Article: In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost](https://docs.titanml.co/blog/speculative-decoding-unleashed/)|⬜|\n|[Article: Accelerating Generative AI with PyTorch II: GPT, Fast](https://pytorch.org/blog/accelerating-generative-ai-2/)|⬜|\n|[Article: Harmonizing Multi-GPUs: Efficient Scaling of LLM Inference](https://docs.titanml.co/blog/multi-gpu/)|⬜|\n|[Article: Multi-Query Attention is All You Need](https://fireworks.ai/blog/multi-query-attention-is-all-you-need)|⬜|\n|[Article: Transformers Inference Optimization Toolset](https://astralord.github.io/posts/transformer-inference-optimization-toolset/)|⬜|\n|[DeepLearning.AI: Efficiently Serving LLMs](https://www.deeplearning.ai/short-courses/efficiently-serving-llms/)|✅|\n|[Article: LLM Inference Series: 3. KV caching explained](https://medium.com/@plienhar/llm-inference-series-3-kv-caching-unveiled-048152e461c8)|⬜|\n|[Article: LLM Inference Series: 4. KV caching, a deeper look](https://medium.com/@plienhar/llm-inference-series-4-kv-caching-a-deeper-look-4ba9a77746c8)|⬜|\n|[Article: LLM Inference Series: 5. Dissecting model performance](https://medium.com/@plienhar/llm-inference-series-5-dissecting-model-performance-6144aa93168f)|⬜|\n|[Article: Transformer Inference Arithmetic](https://kipp.ly/transformer-inference-arithmetic/)|⬜|\n|[Article: Optimizing AI Inference at Character.AI](https://research.character.ai/optimizing-inference/)|⬜|\n|[Article: Optimizing AI Inference at Character.AI (Part Deux)](https://research.character.ai/optimizing-ai-inference-at-character-ai-part-deux/)|⬜|\n|[Article: llama.cpp guide - Running LLMs locally, on any hardware, from scratch](https://blog.steelph0enix.dev/posts/llama-cpp-guide/)|✅|\n|[Youtube: SBTB 2023: Charles Frye, Parallel Processors: Past \u0026 Future Connections Between LLMs and OS Kernels](https://www.youtube.com/watch?v=VxFtHqlMv8c)|✅|\n|[Youtube: Deploying Fine-Tuned Models](https://youtu.be/GzEcyBykkdo) `2h28m`|✅|\n\n\n#### Evals and Guardrails\n\n|Resource|Progress|\n|---|---|\n|[Article: Your AI Product Needs Evals](https://hamel.dev/blog/posts/evals)|✅|\n|[Article: Task-Specific LLM Evals that Do \u0026 Don't Work](https://eugeneyan.com/writing/evals/)|✅|\n|[Article: Evaluation \u0026 Hallucination Detection for Abstractive Summaries](https://eugeneyan.com/writing/abstractive/)|✅|\n|[Article: LLM-as-a-Judge vs Human Evaluation](https://www.galileo.ai/blog/llm-as-a-judge-vs-human-evaluation)|⬜|\n|[DeepLearning.AI: Automated Testing for LLMOps](https://www.deeplearning.ai/short-courses/automated-testing-llmops/)|✅|\n|[DeepLearning.AI: Red Teaming LLM Applications](https://www.deeplearning.ai/short-courses/red-teaming-llm-applications/)|✅|\n|[DeepLearning.AI: Evaluating and Debugging Generative AI Models Using Weights and Biases](https://www.deeplearning.ai/short-courses/evaluating-debugging-generative-ai/)|⬜|\n|[DeepLearning.AI: Quality and Safety for LLM Applications](https://www.deeplearning.ai/short-courses/quality-safety-llm-applications/)|⬜|\n|[OpenAI: Evals Build Hour](https://vimeo.com/showcase/11333741/video/1023317525)|✅|\n|[Youtube: Instrumenting \u0026 Evaluating LLMs](https://youtu.be/SnbGD677_u0) `2hr33m`|✅|\n|[Youtube: LLM Eval For Text2SQL](https://youtu.be/UGmenkjGXqM?list=PLgIaq8VgndJvt-HKMHPXehyJNNXQsAVHD) `51m`|✅|\n|[Youtube: A Deep Dive on LLM Evaluation](https://youtu.be/IsZVCnViwhk?list=PLgIaq8VgndJvt-HKMHPXehyJNNXQsAVHD) `49m`|✅|\n\n### Finetuning and Distillation\n\n|Resource|Progress|\n|---|---|\n|[Article: Tokenization Gotchas](https://hamel.dev/notes/llm/finetuning/tokenizer_gotchas.html)|⬜|\n|[Article: Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)](https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms)|⬜|\n|[OpenAI: GPT-4o mini Fine-Tuning Build Hour](https://vimeo.com/showcase/11333741/video/995989828)|✅|\n|[OpenAI: Distillation Build Hour](https://vimeo.com/showcase/11333741/video/1029408095)|✅|\n|[Article: How to Generate and Use Synthetic Data for Finetuning](https://eugeneyan.com/writing/synthetic/)|✅|\n|[DeepLearning.AI: Finetuning Large Language Models](https://www.deeplearning.ai/short-courses/finetuning-large-language-models/)|✅|\n|[Youtube: Fine-Tuning with Axolotl](https://youtu.be/mmsa4wDsiy0?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC) `2h10m`|✅|\n|[Youtube: Creating, Curating, and Cleaning Data for LLMs](https://youtu.be/HEGaei7k0zE?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC) `54m`|✅|\n|[Youtube: Best Practices For Fine Tuning Mistral](https://youtu.be/Z_oWzTuljss?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC) `23m`|✅|\n|[Youtube: Fine Tuning OpenAI Models - Best Practices](https://youtu.be/Q0GSZD0Na1s?list=PLgIaq8VgndJtZ_G6gxyuhHGLUy9zXV9JC)|✅|\n|[Youtube: When and Why to Fine Tune an LLM](https://youtu.be/cPn0nHFsvFg) `1h56m`|✅|\n|[Youtube: Napkin Math For Fine Tuning Pt. 1 w/Johno Whitaker](https://youtu.be/-2ebSQROew4)|✅|\n|[Youtube: Napkin Math For Fine Tuning Pt. 2 w/Johno Whitaker](https://youtu.be/u2fJ6K8FjS8)|✅|\n|[Youtube: Fine Tuning LLMs for Function Calling w/Pawel Garback](https://youtu.be/SEZ7j31u67A) `1h32m`|✅|\n|[Youtube: From Prompt to Model: Fine-tuning when you've already deployed LLMs in prod w/Kyle Corbitt](https://youtu.be/4EPZZkVrXC4) `32m`|✅|\n|[Youtube: Why Fine Tuning is Dead w/Emmanuel Ameisen](https://youtu.be/h1c_jmk97Ss) `50m`|✅|\n|[Benchmarking QLoRA+FSDP](https://github.com/AnswerDotAI/fsdp_qlora/blob/main/benchmarks_03_2024.md)|⬜|\n\n#### LLM System Design\n\n|Resource|Progress|\n|---|---|\n|[Article: What We’ve Learned From A Year of Building with LLMs](https://applied-llms.org/)|⬜|\n|[Article: Data Flywheels for LLM Applications](https://www.sh-reya.com/blog/ai-engineering-flywheel/)|⬜|\n|[Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy](https://www.godaddy.com/resources/news/llm-from-the-trenches-10-lessons-learned-operationalizing-models-at-godaddy#h-3-prompts-aren-t-portable-across-models)|✅|\n|[Article: Emerging UX Patterns for Generative AI Apps \u0026 Copilots](https://www.tidepool.so/blog/emerging-ux-patterns-for-generative-ai-apps-copilots)|✅|\n|[Article: The Novice's LLM Training Guide](https://rentry.co/llm-training)|⬜|\n|[Article: Pushing ChatGPT's Structured Data Support To Its Limits](https://minimaxir.com/2023/12/chatgpt-structured-data/)|✅|\n|[Article: GPTed: using GPT-3 for semantic prose-checking](https://vgel.me/posts/gpted-launch/)|✅|\n|[Article: Don't worry about LLMs](https://vickiboykis.com/2024/05/20/dont-worry-about-llms/)|⬜|\n|[Article: Things we learned about LLMs in 2024](https://simonwillison.net/2024/Dec/31/llms-in-2024/)|⬜|\n|[Article: Data acquisition strategies for AI-first start-ups](https://press.airstreet.com/p/data-acquisition-strategies-for-ai?utm_source=substack\u0026utm_medium=email)|⬜|\n|[DeepLearning.AI: Building Systems with the ChatGPT API](https://www.deeplearning.ai/short-courses/building-systems-with-chatgpt/)|⬜|\n|[DeepLearning.AI: Building Generative AI Applications with Gradio](https://www.deeplearning.ai/short-courses/building-generative-ai-applications-with-gradio/)|✅|\n|[DeepLearning.AI: Open Source Models with Hugging Face](https://www.deeplearning.ai/short-courses/open-source-models-hugging-face/)|⬜|\n|[DeepLearning.AI: Getting Started with Mistral](https://www.deeplearning.ai/short-courses/getting-started-with-mistral/)|⬜|\n|[LLMOps: Building with LLMs](https://www.comet.com/site/llm-course/)|⬜|\n|[LLM Bootcamp - Spring 2023](https://fullstackdeeplearning.com/llm-bootcamp/spring-2023/)|✅|\n|[Youtube: A Survey of Techniques for Maximizing LLM Performance](https://www.youtube.com/watch?v=ahnGLM-RC1Y)|✅|\n|[Youtube: Building Blocks for LLM Systems \u0026 Products: Eugene Yan](https://www.youtube.com/watch?v=LzeC1AQ-U5o)|✅|\n|[Youtube: Building LLM Applications](https://www.youtube.com/playlist?list=PLgIaq8VgndJtrxcelEdnXbvh9fXMHeAps)|0/8|\n|[Article: Emerging Architectures for LLM Applications](https://a16z.com/emerging-architectures-for-llm-applications/)|✅|\n|[Article: Patterns for Building LLM-based Systems \u0026 Products](https://eugeneyan.com/writing/llm-patterns/)|✅|\n|[DeepLearning.AI: LLMOps](https://www.deeplearning.ai/short-courses/llmops/)|⬜|\n|[DeepLearning.AI: Serverless LLM apps with Amazon Bedrock](https://www.deeplearning.ai/short-courses/serverless-llm-apps-amazon-bedrock/)|⬜|\n|[Youtube: Getting the Most Out of Your LLM Experiments](https://youtu.be/IfcDvtl6Z1Y) `48m`|✅|\n\n\n## Technical Skills (Libraries/Frameworks/Tools)\n\n### AWS\n\n\n|Resource|Progress|\n|---|---|\n|[Udemy: AWS Certified Developer - Associate 2018](https://www.udemy.com/aws-certified-developer-associate/)|✅|\n\n### CSS\n\n|Resource|Progress|\n|---|---|\n|[Pluralsight: CSS Positioning](https://www.pluralsight.com/courses/css-positioning-1834)|✅|\n|[Pluralsight: Introduction to CSS](https://www.pluralsight.com/courses/css-intro)|✅|\n|[Pluralsight: CSS: Specificity, the Box Model, and Best Practices](https://app.pluralsight.com/interactive-courses/detail/c580b092-d94a-4ed8-8d2a-2f4d0b76f99f)|✅|\n|[Pluralsight: CSS: Using Flexbox for Layout](https://app.pluralsight.com/interactive-courses/detail/a089d0a5-4a4c-4c4e-b883-c1bc64009619)|✅|\n|[Code School: Blasting Off with Bootstrap](https://www.pluralsight.com/courses/code-school-blasting-off-with-bootstrap)|✅|\n|[Pluralsight: UX Fundamentals](https://www.pluralsight.com/courses/ux-fundamentals-2426)|✅|\n|[Codecademy: Learn SASS](https://www.codecademy.com/learn/learn-sass)|✅|\n|[CSS for Javascript Developers](https://css-for-js.dev/)|✅|\n|[Article: Create an illustration in Figma design](https://help.figma.com/hc/en-us/articles/13543867954711-Create-an-illustration-in-Figma-design)|✅|\n|[Book: Refactoring UI](https://refactoringui.com/book/)|⬜|\n|[Youtube: How to Make Your Website Not Ugly: Basic UX for Programmers](https://www.youtube.com/watch?v=Jf0cjocP8Wk) `48m`|⬜|\n\n\n\n### Django\n\n|Resource|Progress|\n|---|---|\n|[Article: Django, HTMX and Alpine.js: Modern websites, JavaScript optional](https://www.saaspegasus.com/guides/modern-javascript-for-django-developers/htmx-alpine/)|✅|\n\n### HTML\n\n|Resource|Progress|\n|---|---|\n|[Codecademy: Learn HTML](https://www.codecademy.com/learn/learn-html)|✅|\n|[Codecademy: Make a website](https://www.codecademy.com/en/courses/make-a-website)|✅|\n|[Article: Alternative Text](https://webaim.org/techniques/alttext/)|⬜|\n\n### Langchain\n\n|Resource|Progress|\n|---|---|\n|[Pinecone: LangChain AI Handbook](https://www.pinecone.io/learn/series/langchain/)|0/11|\n|[DeepLearning.AI: LangChain for LLM Application Development](https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/)|⬜|\n|[DeepLearning.AI: LangChain: Chat with Your Data](https://www.deeplearning.ai/short-courses/langchain-chat-with-your-data/)|⬜|\n\n\n### JavaScript\n\n|Resource|Progress|\n|---|---|\n|[Udacity: ES6 - JavaScript Improved](https://www.udacity.com/course/es6-javascript-improved--ud356)|✅|\n|[Udacity: Intro to Javascript](https://www.udacity.com/course/intro-to-javascript--ud803)|✅|\n|[Udacity: Object Oriented JS 1](https://www.udacity.com/course/object-oriented-javascript--ud015)|✅|\n|[Udacity: Object Oriented JS 2](https://www.udacity.com/course/object-oriented-javascript--ud711)|✅|\n|[Udemy: Understanding Typescript](https://www.udemy.com/understanding-typescript/)|✅|\n|[Codecademy: Learn JavaScript](https://www.codecademy.com/learn/learn-javascript)|✅|\n|[Codecademy: Jquery Track](https://www.codecademy.com/learn/learn-jquery)|✅|\n|[Pluralsight: Using The Chrome Developer Tools](https://www.pluralsight.com/courses/chrome-developer-tools)|✅|\n\n\n### Matplotlib\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Introduction to Seaborn](https://www.datacamp.com/courses/introduction-to-seaborn)|✅|\n|[Datacamp: Introduction to Matplotlib](https://www.datacamp.com/courses/introduction-to-matplotlib)|✅|\n\n\n### MLFlow\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Introduction to MLFlow](https://www.datacamp.com/courses/introduction-to-mlflow)|✅|\n\n\n### Numpy\n\n|Resource|Progress|\n|---|---|\n|[Youtube: Numpy Array Broadcasting In Python Explained](https://youtu.be/oG1t3qlzq14)|✅|\n\n\n### Nexxt.JS\n\n| Resource                                                          | Progress |\n| ----------------------------------------------------------------- | -------- |\n| [Docs: Start building with Next.js](https://nextjs.org/learn) |          |\n\n### Pandas\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Pandas Foundations](https://www.datacamp.com/courses/pandas-foundations)|✅|\n|[Datacamp: Pandas Joins for Spreadsheet Users](https://www.datacamp.com/courses/pandas-joins-for-spreadsheet-users)|✅|\n|[Datacamp: Manipulating DataFrames with pandas](https://www.datacamp.com/courses/manipulating-dataframes-with-pandas)|✅|\n|[Datacamp: Merging DataFrames with pandas](https://www.datacamp.com/courses/merging-dataframes-with-pandas)|✅|\n|[Datacamp: Data Manipulation with pandas](https://www.datacamp.com/courses/data-manipulation-with-pandas)|✅|\n|[Datacamp: Optimizing Python Code with pandas](https://www.datacamp.com/courses/optimizing-python-code-with-pandas)|✅|\n|[Datacamp: Streamlined Data Ingestion with pandas](https://www.datacamp.com/courses/streamlined-data-ingestion-with-pandas)|✅|\n|[Datacamp: Analyzing Marketing Campaigns with pandas](https://www.datacamp.com/courses/analyzing-marketing-campaigns-with-pandas)|✅|\n|[Datacamp: Analyzing Police Activity with pandas](https://www.datacamp.com/courses/analyzing-police-activity-with-pandas)|✅|\n\n\n### PyTorch\n\n|Resource|Progress|\n|---|---|\n|[Article: PyTorch internals](https://blog.ezyang.com/2019/05/pytorch-internals/)|⬜|\n|[Article: Taking PyTorch For Granted](https://nrehiew.github.io/blog/pytorch/)|⬜|\n|[Datacamp: Introduction to Deep Learning with PyTorch](https://www.datacamp.com/courses/deep-learning-with-pytorch)|✅|\n|[Datacamp: Intermediate Deep Learning with PyTorch](https://app.datacamp.com/learn/courses/intermediate-deep-learning-with-pytorch)|⬜|\n|[Datacamp: Deep Learning for Text with PyTorch](https://www.datacamp.com/courses/deep-learning-for-text-with-pytorch)|⬜|\n|[Datacamp: Deep Learning for Images with PyTorch](https://www.datacamp.com/courses/deep-learning-for-images-with-pytorch)|⬜|\n|[Deeplizard: Neural Network Programming - Deep Learning with PyTorch](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG)|✅|\n\n\n### ReactJS\n\n|Resource|Progress|\n|---|---|\n|[Codecademy: Learn ReactJS: Part I](https://www.codecademy.com/learn/react-101)|✅|\n|[Codecademy: Learn ReactJS: Part II](https://www.codecademy.com/learn/react-102)|✅|\n|[NexxtJS: React Foundations](https://nextjs.org/learn/react-foundations)|⬜|\n\n### Spacy\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Advanced NLP with spaCy](https://www.datacamp.com/courses/advanced-nlp-with-spacy)|✅|\n\n### Tensorflow \u0026 Keras\n\n|Resource|Progress|\n|---|---|\n|[Datacamp: Introduction to TensorFlow in Python](https://www.datacamp.com/courses/introduction-to-tensorflow-in-python)|✅|\n|[Datacamp: Deep Learning in Python](https://www.datacamp.com/courses/deep-learning-in-python)|✅|\n|[Datacamp: Introduction to Deep Learning with Keras](https://www.datacamp.com/courses/deep-learning-with-keras-in-python)|✅|\n|[Datacamp: Advanced Deep Learning with Keras](https://www.datacamp.com/courses/advanced-deep-learning-with-keras-in-python)|✅|\n|[Deeplizard: Keras - Python Deep Learning Neural Network API](https://www.youtube.com/playlist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)|✅|\n|[Udacity: Intro to TensorFlow for Deep Learning](https://www.udacity.com/course/intro-to-tensorflow-for-deep-learning--ud187)|✅|\n\n### VSCode\n\n|Resource|Progress|\n|---|---|\n|[VSCode Docs: Python Interactive window](https://code.visualstudio.com/docs/python/jupyter-support-py)|⬜|\n\n## Miscellaneous\n\n### Design\n\n|Resource|Progress|\n|---|---|\n|[Course: How to Visualize Value](https://visualizevalue.com/products/how-to-visualize-value)|✅|\n\n### Finance\n\n|Resource|Progress|\n|---|---|\n|[Coursera: Financial Markets](https://www.coursera.org/learn/financial-markets-global)|⬜|\n\n\n\n### Marketing\n\n|Resource|Progress|\n|---|---|\n|[Course: Build Once, Sell Twice](https://visualizevalue.com/products/build-once-sell-twice-the-productization-playbook)|✅|\n\n### Search Engine Optimization (SEO)\n\n|Resource|Progress|\n|---|---|\n|[Course: Compound Content](https://visualizevalue.com/products/compound-content)|✅|\n\n### Technical Writing\n|Resource|Progress|\n|---|---|\n|[Google: Technical Writing Course](https://developers.google.com/tech-writing/overview)|⬜|","funding_links":[],"categories":["Others","A01_机器学习教程","Deep Learning Repositories","学习资料"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famitness%2Flearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famitness%2Flearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famitness%2Flearning/lists"}