GitHub - amitness/learning: A log of things I'm learning

learning

A running log of things I'm learning to build strong core software engineering skills while also expanding my knowledge of adjacent technologies a little bit everyday.

Updated: Once a month | Current Focus: Generative AI

Core Skills

Generic transferable skills independent of a stack

Python Programming

Resource	Progress
Datacamp: Writing Efficient Python Code	✅
Datacamp: Writing Functions in Python	✅
Datacamp: Object-Oriented Programming in Python	✅
Datacamp: Intermediate Object-Oriented Programming in Python	✅
Datacamp: Importing Data in Python (Part 1)	✅
Datacamp: Importing Data in Python (Part 2)	✅
Datacamp: Intermediate Python for Data Science	✅
Datacamp: Python Data Science Toolbox (Part 1)	✅
Datacamp: Python Data Science Toolbox (Part 2)	✅
Datacamp: Developing Python Packages	✅
Datacamp: Conda Essentials	✅
Youtube: Tutorial: Sebastian Witowski - Modern Python Developer's Toolkit	✅
Datacamp: Working with Dates and Times in Python	✅
Datacamp: Command Line Automation in Python	⬜
Book: Python 201	⬜
Book: Writing Idiomatic Python 3	⬜
Article: Python's many command-line utilities	⬜
Article: A Programmer’s Introduction to Unicode	⬜
Article: Exposing string types to maximize user happiness	✅

Testing & Profiling

Resource	Progress
Datacamp: Unit Testing for Data Science in Python	✅
Article: Introduction to Memory Profiling in Python	✅
Article: Profiling Python code with memory_profiler	✅
Article: How to Use "memory_profiler" to Profile Memory Usage by Python Code?	✅
Youtube: Debug Python inside Docker using debugpy and VSCode	✅
Article: Concurrency For Starlette Apps (e.g FastAPI / FastHTML)	✅

Data Structures and Algorithms

Resource	Progress
Book: Grokking Algorithms	✅
Book: The Tech Resume Inside Out	✅
Neetcode: Algorithms and Data Structures for Beginners	✅
Neetcode: Advanced Algorithms `1/7`	⬜
Udacity: Intro to Data Structures and Algorithms	✅
Youtube: Sliding Window Technique - Algorithmic Mental Models `36:44`	✅

Linux & Command Line

Resource	Progress
Datacamp: Introduction to Shell for Data Science	✅
Datacamp: Introduction to Bash Scripting	✅
Datacamp: Data Processing in Shell	✅
MIT: The Missing Semester	✅
Udacity: Linux Command Line Basics	✅
Udacity: Shell Workshop	✅
Udacity: Configuring Linux Web Servers	✅
Article: A guide to manage your environment variables in a better way using direnv	✅

Version Control

Resource	Progress
Udacity: Version Control with Git	✅
Datacamp: Introduction to Git for Data Science	✅
Udacity: GitHub & Collaboration	✅
Udacity: How to Use Git and GitHub	✅
Youtube: How to Use Git Worktree \| Checkout Multiple Git Branches at Once	✅
Datacamp: Advanced Git	✅

Databases

Resource	Progress
Udacity: Intro to relational database	✅
Udacity: Database Systems Concepts & Design	⬜
Datacamp: Database Design	✅
Datacamp: Introduction to Databases in Python	⬜
Datacamp: Intro to SQL for Data Science	✅
Datacamp: Intermediate SQL	✅
Datacamp: Joining Data in SQL	✅
Datacamp: Data Manipulation in SQL	⬜
Udacity: SQL for Data Analysis	⬜
Datacamp: Exploratory Data Analysis in SQL	⬜
Datacamp: Applying SQL to Real-World Problems	⬜
Datacamp: Analyzing Business Data in SQL	⬜
Datacamp: Reporting in SQL	⬜
Datacamp: Data-Driven Decision Making in SQL	⬜
Datacamp: NoSQL Concepts	✅
Datacamp: Introduction to MongoDB in Python	⬜

System Design

Resource	Progress
Book: Designing Machine Learning Systems	✅
Neetcode: System Design for Beginners	✅
Neetcode: System Design Interview	✅
Datacamp: Customer Analytics & A/B Testing in Python	✅
Datacamp: A/B Testing in Python	⬜
Udacity: A/B Testing	⬜
Datacamp: MLOps Concepts	✅
Datacamp: Machine Learning Monitoring Concepts	✅
Udacity: Authentication & Authorization: OAuth	✅
Udacity: HTTP & Web Servers	✅
Udacity: Client-Server Communication	⬜
Udacity: Designing RESTful APIs	✅
Udacity: Networking for Web Developers	✅

Maths

Resource	Progress
Datacamp: Foundations of Probability in Python	✅
Datacamp: Introduction to Statistics	✅
Datacamp: Introduction to Statistics in Python	✅
Datacamp: Hypothesis Testing in Python	✅
Datacamp: Statistical Thinking in Python (Part 1)	✅
Datacamp: Statistical Thinking in Python (Part 2)	✅
Datacamp: Experimental Design in Python	✅
Datacamp: Practicing Statistics Interview Questions in Python	⬜
edX: Essential Statistics for Data Analysis using Excel	✅
Udacity: Intro to Inferential Statistics	✅
MIT 18.06 Linear Algebra, Spring 2005	✅
Udacity: Eigenvectors and Eigenvalues	✅
Udacity: Linear Algebra Refresher	⬜
Youtube: Essence of linear algebra	⬜

Agentic Coding

Resource	Progress
DeepLearning.AI: Build Apps with Windsurf’s AI Coding Agents `1h10m`	✅
Youtube: Prompt Driven Development Series `9/9`	✅

Specialization

Traditional Machine Learning

Resource	Progress
Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition	⬜
Book: A Machine Learning Primer	✅
Book: Grokking Machine Learning	✅
Book: The StatQuest Illustrated Guide To Machine Learning	✅
Datacamp: Ensemble Methods in Python	✅
Datacamp: Extreme Gradient Boosting with XGBoost	⬜
Datacamp: Clustering Methods with SciPy	✅
Datacamp: Unsupervised Learning in Python	✅
Udacity: Segmentation and Clustering	✅
Datacamp: Intro to Python for Data Science	✅
edX: Implementing Predictive Analytics with Spark in Azure HDInsight	✅
Datacamp: Supervised Learning with scikit-learn	✅
Datacamp: Machine Learning with Tree-Based Models in Python	✅
Datacamp: Linear Classifiers in Python	✅
Datacamp: Model Validation in Python	✅
Datacamp: Hyperparameter Tuning in Python	✅
Datacamp: HR Analytics in Python: Predicting Employee Churn	✅
Datacamp: Predicting Customer Churn in Python	✅
Datacamp: Dimensionality Reduction in Python	✅
Datacamp: Preprocessing for Machine Learning in Python	✅
Datacamp: Data Types for Data Science	✅
Datacamp: Cleaning Data in Python	✅
Datacamp: Feature Engineering for Machine Learning in Python	✅
Datacamp: Predicting CTR with Machine Learning in Python	✅
Datacamp: Intro to Financial Concepts using Python	✅
Datacamp: Fraud Detection in Python	✅
Article: The wrong batch size is all it takes	✅
Article: A Gentle Introduction to Expectation-Maximization (EM Algorithm)	✅
Article: How to Use Out-of-Fold Predictions in Machine Learning	✅
Article: Stacking and Blending — An Intuitive Explanation	✅

Deep Learning

Resource	Progress
Article: An overview of gradient descent optimization algorithms	✅
Book: Make Your Own Neural Network	✅
Fast.ai: Practical Deep Learning for Coder (Part 1)	✅
Fast.ai: Practical Deep Learning for Coder (Part 2) `9, 13,14,17,18(48:10),19`	⬜
Datacamp: Convolutional Neural Networks for Image Processing	✅
Karpathy: Neural Networks: Zero to Hero	✅
Article: Things that confused me about cross-entropy	✅
Article: Why is the ReLU function not differentiable at x=0?	✅
Article: Are CNNs invariant to translation, rotation, and scaling?	✅
Article: How to Control the Stability of Training Neural Networks With the Batch Size	✅
Article: A Visual Guide to Learning Rate Schedulers in PyTorch	✅

Natural Language Processing

Resource	Progress
Book: Natural Language Processing with Transformers	✅
Stanford CS224U: Natural Language Understanding \| Spring 2019 `15/15 lectures`	✅
Stanford CS224N: Stanford CS224N: NLP with Deep Learning \| Winter 2019 `22/22 lectures`	✅
CMU: Low-resource NLP Bootcamp 2020 `8/8 lectures`	✅
CMU Multilingual NLP 2020	✅
Datacamp: Feature Engineering for NLP in Python	✅
Datacamp: Natural Language Processing Fundamentals in Python	✅
Datacamp: Regular Expressions in Python	✅
Datacamp: RNN for Language Modeling	✅
Datacamp: Natural Language Generation in Python	✅
Datacamp: Building Chatbots in Python	✅
Datacamp: Sentiment Analysis in Python	✅
Datacamp: Machine Translation in Python	✅
Article: The Unreasonable Effectiveness of Collocations	⬜
Article: FuzzyWuzzy: Fuzzy String Matching in Python	✅
Article: Transformers: Origins	⬜
Notebook: Understanding the Difference Between Embedding Layers and Linear Layers	✅

Generative AI

LLM Theory

Resource	Progress
Book: Hands-On Large Language Models: Language Understanding and Generation	✅
Book: Large Language Models: A Deep Dive: Bridging Theory and Practice	⬜
Book: Super Study Guide: Transformers & Large Language Models	✅
Book: The Hundred-Page Language Models Book	✅
Book: Build a Large Language Model (From Scratch)	`246/581`
The Smol Training Playbook: The Secrets to Building World-Class LLMs	⬜
Article: You could have designed state of the art Positional Encoding	✅
Article: From Digits to Decisions: How Tokenization Impacts Arithmetic in LLMs	✅
Article: SolidGoldMagikarp (plus, prompt generation)	✅
Article: Sampling for Text Generation	✅
Article: First Token Cutoff LLM sampling	✅
Article: The Big LLM Architecture Comparison	✅
Article: From GPT-2 to gpt-oss: Analyzing the Architectural Advances	✅
Article: A Visual Guide to Mamba and State Space Models	⬜
Article: Patterns and Messages - Part 1 - The Missing Subscript	✅
Article: How text diffusion works	✅
Article: The Illustrated Evo 2	⬜
Article: Interpreting the Prediction of BERT Model for Text Classification	✅
Article: 2025: The year in LLMs	✅
Article: Things we learned about LLMs in 2024	⬜
DeepLearning.AI: Pretraining LLMs	✅
DeepLearning.AI: Reinforcement Learning from Human Feedback	✅
DeepLearning.AI: How Transformer LLMs Work	✅
Karpathy: Intro to Large Language Models `1hr`	✅
Karpathy: Let's build the GPT Tokenizer `2hr13m`	✅
Karpathy: Let's reproduce GPT-2 (124M) `4hr1m`	✅
Youtube: A Hackers' Guide to Language Models `1hr30m`	✅
Karpathy: Deep Dive into LLMs like ChatGPT `3h31m`	✅
Youtube: 5 Years of GPTs with Finbarr Timbers `55m`	✅
Youtube: Stanford CS229 I Machine Learning I Building Large Language Models (LLMs) `1h44m`	✅
Youtube: LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU `1h10m`	✅
Youtube: CMU Advanced NLP Fall 2024 (14): Ensembling and Mixture of Experts	✅
Youtube: A little guide to building Large Language Models in 2024 `1h15m`	✅
Youtube: How I use LLMs `2h7m`	✅
Youtube: Simple Diffusion Language Models	✅
Youtube: Zed Inferred: Diffusion Language Models	✅

Post-training (RLHF / RLVR)

Resource	Progress
Book: A Little Bit of Reinforcement Learning from Human Feedback	✅
Course: Post-training of LLMs	✅
Article: Scaling test-time compute - a Hugging Face Space by HuggingFaceH4	✅
Article: DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs	✅
Article: The Illustrated DeepSeek-R1	✅
Article: A Visual Guide to Reasoning LLMs	✅
Article: GRPO in DeepSeek-R1	✅
Youtube: How DeepSeek Changes the LLM Story	✅
Youtube: Speculations on Test-Time Scaling (o1) `47m`	✅
Youtube: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning `1h19m`	✅
Youtube: MIT EI seminar, Hyung Won Chung from OpenAI. "Don't teach. Incentivize." `35m`	✅
Youtube: Group Relative Policy Optimization (GRPO) - Formula and Code `24m`	✅
Youtube: How to approach post-training for AI applications `22m`	✅

Multi-modality (Vision)

Resource	Progress
Article: Understanding Multimodal LLMs	✅
Article: Computer-Using Agent	✅
Article: Flow Matching in 5 Minutes	✅
Article: Understanding Patch Embeddings for Vision Transformers (ViT)	✅
Youtube: AI Visions Live \| Merve Noyan \| Open-source Multimodality `54m`	✅
DeepLearning.AI: How Diffusion Models Work	✅
DeepLearning.AI: Prompt Engineering for Vision Models	⬜
DeepLearning.AI: Building Multimodal Search and RAG	✅
Pinecone: Embedding Methods for Image Search	0/8
Youtube: Lesson 9A 2022 - Stable Diffusion deep dive	✅
Article: Diffusion models are autoencoders	✅
Article: Diffusion Language Models	✅
Article: Guidance: a cheat code for diffusion models	✅
Article: Perspectives on diffusion	✅
Article: The geometry of diffusion guidance	✅
Article: Diffusion is spectral autoregression	✅
Article: Generative modelling in latent space	✅
Youtube: Sander Dieleman - Generative modelling through iterative refinement	✅

Multi-modality (Audio)

Resource	Progress
Article: Speech AI models: an introduction	⬜
Article: Voice AI & Voice Agents - An Illustrated Primer	⬜
Article: Neural audio codecs: how to get audio into LLMs	⬜

Quantization

Resource	Progress
Article: Quantization Fundamentals with Hugging Face	✅
DeepLearning.AI: Quantization in Depth	⬜
DeepLearning.AI: Introduction to On-Device AI	⬜
Article: A Visual Guide to Quantization	⬜
Article: QLoRA and 4-bit Quantization	⬜
Article: Understanding AI/LLM Quantisation Through Interactive Visualisations	⬜
Youtube: CMU Advanced NLP Fall 2024 (11): Distillation, Quantization, and Pruning	⬜
Article: LLM.int8() and Emergent Features	⬜

Distributed Training

Resource	Progress
Youtube: Slaying OOMs with PyTorch FSDP and torchao `49m`	✅
Youtube: Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code `1h12m`	✅
Youtube: How DDP works \|\| Distributed Data Parallel	✅
Youtube: FSDP Explained	✅
Youtube: Lecture 48: The Ultra Scale Playbook `3h3m`	`44:24`
Youtube: Invited Talk: PyTorch Distributed (DDP, RPC) - By Facebook Research Scientist Shen Li	✅
Youtube: Unit 9 \| Techniques for Speeding Up Model Training	✅
Article: A Short Guide to PyTorch DDP	✅
Article: Scaling Deep Learning with PyTorch: Multi-Node and Multi-GPU Training Explained (with Code)	✅
Article: Accelerating PyTorch Model Training	✅
Article: Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow	✅
Article: Distributed data parallel training in Pytorch	✅
Article: Training on Multiple GPUs	✅
Article: From Single GPU to Clusters: A Practical Journey into Distributed Training with PyTorch and Ray	⬜

Parallel Computing

Resource	Progress
Udacity: Intro to Parallel Programming `458 videos`	299/458
Book: Programming Massively Parallel Processors: A Hands-on Approach	Ch. 2
Book: The Algebra of Speed	⬜
Youtube: GPU Puzzles: Let's Play	⬜

Inference Optimization

Resource	Progress
Article: How to make LLMs go fast	✅
Article: In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost	⬜
Article: Accelerating Generative AI with PyTorch II: GPT, Fast	⬜
Article: Harmonizing Multi-GPUs: Efficient Scaling of LLM Inference	⬜
Article: Multi-Query Attention is All You Need	⬜
Article: Transformers Inference Optimization Toolset	⬜
DeepLearning.AI: Efficiently Serving LLMs	✅
Article: LLM Inference Series: 3. KV caching explained	⬜
Article: LLM Inference Series: 4. KV caching, a deeper look	⬜
Article: LLM Inference Series: 5. Dissecting model performance	⬜
Article: Transformer Inference Arithmetic	⬜
Article: Optimizing AI Inference at Character.AI	⬜
Article: Optimizing AI Inference at Character.AI (Part Deux)	⬜
Article: llama.cpp guide - Running LLMs locally, on any hardware, from scratch	✅
Article: Domain specific architectures for AI inference	⬜
Youtube: SBTB 2023: Charles Frye, Parallel Processors: Past & Future Connections Between LLMs and OS Kernels	✅
Youtube: Deploying Fine-Tuned Models `2h28m`	✅
Article: Compiling ML models to C for fun	⬜
Article: How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog	⬜
Article: Inside vLLM: Anatomy of a High-Throughput LLM Inference System	⬜
Article: Defeating Nondeterminism in LLM Inference	⬜
Article: Prompt caching: 10x cheaper LLM tokens, but how?	⬜
Article: Attention Mechanism: From Math to GPU	⬜

Finetuning and Distillation

Resource	Progress
Article: Tokenization Gotchas	⬜
Article: Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation)	⬜
OpenAI: GPT-4o mini Fine-Tuning Build Hour	✅
OpenAI: Distillation Build Hour	✅
Article: How to Generate and Use Synthetic Data for Finetuning	✅
DeepLearning.AI: Finetuning Large Language Models	✅
Youtube: Fine-Tuning with Axolotl `2h10m`	✅
Youtube: Creating, Curating, and Cleaning Data for LLMs `54m`	✅
Youtube: Best Practices For Fine Tuning Mistral `23m`	✅
Youtube: Fine Tuning OpenAI Models - Best Practices	✅
Youtube: When and Why to Fine Tune an LLM `1h56m`	✅
Youtube: Napkin Math For Fine Tuning Pt. 1 w/Johno Whitaker	✅
Youtube: Napkin Math For Fine Tuning Pt. 2 w/Johno Whitaker	✅
Youtube: Fine Tuning LLMs for Function Calling w/Pawel Garback `1h32m`	✅
Youtube: From Prompt to Model: Fine-tuning when you've already deployed LLMs in prod w/Kyle Corbitt `32m`	✅
Youtube: Why Fine Tuning is Dead w/Emmanuel Ameisen `50m`	✅
Benchmarking QLoRA+FSDP	⬜

AI Engineering

LLM System Design

Resource	Progress
Book: AI Engineering: Building Applications with Foundation Models	✅
Book: Designing Large Language Model Applications	⬜
Course: LLM Bootcamp - Spring 2023	✅
Article: What We’ve Learned From A Year of Building with LLMs	✅
Article: Data Flywheels for LLM Applications	⬜
Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy	✅
Article: Emerging UX Patterns for Generative AI Apps & Copilots	✅
Article: The Novice's LLM Training Guide	⬜
Article: Pushing ChatGPT's Structured Data Support To Its Limits	✅
Article: GPTed: using GPT-3 for semantic prose-checking	✅
Article: Don't worry about LLMs	⬜
Article: Data acquisition strategies for AI-first start-ups	⬜
Article: All about synthetic data generation	✅
DeepLearning.AI: Building Systems with the ChatGPT API	⬜
DeepLearning.AI: Building Generative AI Applications with Gradio	✅
Youtube: A Survey of Techniques for Maximizing LLM Performance	✅
Youtube: Building Blocks for LLM Systems & Products: Eugene Yan	✅
Youtube: Building LLM Applications	0/8
Article: Emerging Architectures for LLM Applications	✅
Article: Patterns for Building LLM-based Systems & Products	✅
DeepLearning.AI: LLMOps	⬜
DeepLearning.AI: Serverless LLM apps with Amazon Bedrock	⬜
Youtube: Getting the Most Out of Your LLM Experiments `48m`	✅

Evals and Guardrails

Resource	Progress
Article: Understanding the 4 Main Approaches to LLM Evaluation (From Scratch)	✅
Article: Your AI Product Needs Evals	✅
Article: Task-Specific LLM Evals that Do & Don't Work	✅
Article: Evaluation & Hallucination Detection for Abstractive Summaries	✅
Article: Evaluating the Effectiveness of LLM-Evaluators (aka LLM-as-Judge)	⬜
Article: Aligning LLM as judge with human evaluators	✅
Article: Hard-Earned Lessons from 2 Years of Improving AI Applications	✅
Article: Evaluating Long-Context Question & Answer Systems	⬜
DeepLearning.AI: Automated Testing for LLMOps	✅
DeepLearning.AI: Red Teaming LLM Applications	✅
DeepLearning.AI: Evaluating and Debugging Generative AI Models Using Weights and Biases	⬜
DeepLearning.AI: Quality and Safety for LLM Applications	⬜
OpenAI: Evals Build Hour	✅
Youtube: Instrumenting & Evaluating LLMs `2hr33m`	✅
Youtube: LLM Eval For Text2SQL `51m`	✅
Youtube: A Deep Dive on LLM Evaluation `49m`	✅

Context Engineering

Resource	Progress
Book: Prompt Engineering for LLMs	⬜
Course: LLM Engineering - Structured Outputs	⬜
Article: OpenAI Prompt Engineering	⬜
Article: Prompting Fundamentals and How to Apply them Effectively	✅
Article: How I came in first on ARC-AGI-Pub using Sonnet 3.5 with Evolutionary Test-time Compute	✅
Anthropic Courses	⬜
Anthropic: The Claude in Amazon Bedrock Course	⬜
Article: Prompt Engineering(Liliang Weng)	✅
Article: Prompt Engineering 201: Advanced methods and toolkits	✅
Article: Optimizing LLMs for accuracy	✅
Article: Primers • Prompt Engineering	⬜
Article: Anyscale Endpoints: JSON Mode and Function calling Features	⬜
Article: Guided text generation with Large Language Models	⬜
Article: Effective context engineering for AI agents	✅
DeepLearning.AI: Reasoning with o1	✅
OpenAI: Reasoning with o1 Build Hour	✅
DeepLearning.AI: ChatGPT Prompt Engineering for Developers	⬜
Youtube: Prompt Engineering Overview `1hr4m`	✅
Youtube: Prompt Engineering Workshop `1h`	✅
Youtube: Context Engineering SF - August 20th, 2025 `4/4 videos`	✅

Information Retrieval / RAG

Resource	Progress
Article: Pretrained Transformer Language Models for Search - part 1	✅
Article: Pretrained Transformer Language Models for Search - part 2	✅
Article: Pretrained Transformer Language Models for Search - part 3	✅
Article: Pretrained Transformer Language Models for Search - part 4	✅
Article: How not to use BERT for Document Ranking	✅
Article: Understanding LanceDB's IVF-PQ index	✅
Article: A little pooling goes a long way for multi-vector representations	✅
Article: Levels of Complexity: RAG Applications	✅
Article: Systematically Improving Your RAG	✅
Article: Stop using LGTM@Few as a metric (Better RAG)	✅
Article: Low-Hanging Fruit for RAG Search	✅
Article: What AI Engineers Should Know about Search	✅
Article: Evaluating Chunking Strategies for Retrieval	✅
Article: Sentence Embeddings. Introduction to Sentence Embeddings	✅
Article: LambdaMART in Depth	⬜
Article: Guided Generation with Outlines	✅
Article: RAG tricks from the trenches	⬜
Article: Retrieval 101	⬜
Article: Understanding the BM25 full text search algorithm	⬜
Arxiv: Ragas: Automated Evaluation of Retrieval Augmented Generation	✅
Course: Fullstack Retrieval	⬜
Course: The RAG Flywheel	⬜
DeepLearning.AI: Building and Evaluating Advanced RAG Applications	✅
DeepLearning.AI: Vector Databases: from Embeddings to Applications	✅
DeepLearning.AI: Advanced Retrieval for AI with Chroma	✅
DeepLearning.AI: Prompt Compression and Query Optimization	✅
DeepLearning.AI: Large Language Models with Semantic Search `1hr`	✅
DeepLearning.AI: Building Applications with Vector Databases	✅
DeepLearning.AI: Knowledge Graphs for RAG	⬜
DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications	⬜
DeepLearning.AI: Embedding Models: From Architecture to Implementation	✅
DeepLearning.AI: Retrieval Optimization - From Tokenization to Vector Quantization	✅
Pinecone: Vector Databases in Production for Busy Engineers	✅
Pinecone: Retrieval Augmented Generation	✅
Pinecone: Faiss: The Missing Manual	✅
Pinecone: Natural Language Processing for Semantic Search	0/13
Youtube: Systematically improving RAG applications	✅
Youtube: Back to Basics for RAG w/ Jo Bergum	✅
Youtube: Beyond the Basics of Retrieval for Augmenting Generation (w/ Ben Clavié)	✅
Youtube: RAG From Scratch `14/14`	✅
Youtube: CMU Advanced NLP Fall 2024 (10): Retrieval and RAG `1h17m`	✅
Guidance: Token Healing	⬜
Youtube: What You See Is What You Search: Vision Language Models for PDF Retrieval [Jo Bergum]	✅

Agentic Engineering

Resource	Progress
Berkeley: CS294/194-196 Large Language Model Agents `0/14 lectures`	⬜
Berkeley: Advanced LLM Agents MOOC `0/12 lectures`	⬜
Article: Tool Invocation - Demonstrating the Marvel of GPT's Flexibility	✅
Article: Introducing smolagents, a simple library to build agents	✅
Article: What Problem Does The Model Context Protocol Solve?	✅
Article: Don’t Build Multi-Agents	✅
Article: Coding Agents 101: The Art of Actually Getting Things Done	✅
Article: What makes Claude Code so damn good (and how to recreate that magic in your agent)!?	✅
Anthropic: Building effective agents	✅
Anthropic: Building Effective Agents Cookbook	✅
Anthropic: Writing effective tools for agents — with agents	✅
OpenAI: Assistants & Agents Build Hour	✅
OpenAI: Function Calling Build Hour	✅
DeepLearning.AI: Agentic AI with Andrew Ng	✅
DeepLearning.AI: Building Agentic RAG with LlamaIndex	✅
DeepLearning.AI: Multi AI Agent Systems with crewAI	✅
DeepLearning.AI: Building Towards Computer Use with Anthropic	✅
DeepLearning.AI: Pydantic for LLM Workflows	✅
DeepLearning.AI: Practical Multi AI Agents and Advanced Use Cases with crewAI	⬜
DeepLearning.AI: LLMs as Operating Systems: Agent Memory	✅
DeepLearning.AI: Serverless Agentic Workflows with Amazon Bedrock	⬜
DeepLearning.AI: AI Agentic Design Patterns with AutoGen	⬜
DeepLearning.AI: AI Agents in LangGraph	⬜
DeepLearning.AI: Building Your Own Database Agent	⬜
DeepLearning.AI: Function-Calling and Data Extraction with LLMs `59m`	✅
DeepLearning.AI: Evaluating AI Agents `2h16m`	✅
DeepLearning.AI: Building AI Browser Agents	⬜
Huggingface: Agents Course	Unit 1
Youtube: How to Evaluate Agents: Galileo’s Agentic Evaluations in Action	✅
Youtube: Agent Response \| LangSmith Evaluation - Part 24	✅
Youtube: Single Step \| LangSmith Evaluation - Part 25	✅
Youtube: Agent Trajectory \| LangSmith Evaluation - Part 26	✅
Youtube: Evaluating Agents and Assistants: The AI Conference	✅
Youtube: How to Build, Evaluate, and Iterate on LLM Agents	✅
Youtube: How Claude Code Works - Jared Zoneraich, PromptLayer	✅

Technical Skills (Libraries/Frameworks/Tools)

AWS

Resource	Progress
Udemy: AWS Certified Developer - Associate 2018	✅

CSS

Resource	Progress
Pluralsight: CSS Positioning	✅
Pluralsight: Introduction to CSS	✅
Pluralsight: CSS: Specificity, the Box Model, and Best Practices	✅
Pluralsight: CSS: Using Flexbox for Layout	✅
Code School: Blasting Off with Bootstrap	✅
Pluralsight: UX Fundamentals	✅
Codecademy: Learn SASS	✅
CSS for Javascript Developers	✅
Book: Refactoring UI	⬜
Youtube: How to Make Your Website Not Ugly: Basic UX for Programmers `48m`	⬜

Django

Resource	Progress
Article: Django, HTMX and Alpine.js: Modern websites, JavaScript optional	✅

HTML

Resource	Progress
Codecademy: Learn HTML	✅
Codecademy: Make a website	✅
Article: Alternative Text	⬜

Langchain

Resource	Progress
Pinecone: LangChain AI Handbook	0/11
DeepLearning.AI: LangChain for LLM Application Development	⬜
DeepLearning.AI: LangChain: Chat with Your Data	⬜

JavaScript

Resource	Progress
Udacity: ES6 - JavaScript Improved	✅
Udacity: Intro to Javascript	✅
Udacity: Object Oriented JS 1	✅
Udacity: Object Oriented JS 2	✅
Udemy: Understanding Typescript	✅
Codecademy: Learn JavaScript	✅
Codecademy: Jquery Track	✅
Pluralsight: Using The Chrome Developer Tools	✅

Matplotlib

Resource	Progress
Datacamp: Introduction to Seaborn	✅
Datacamp: Introduction to Matplotlib	✅

MLFlow

Resource	Progress
Datacamp: Introduction to MLFlow	✅

Numpy

Resource	Progress
Youtube: Numpy Array Broadcasting In Python Explained	✅

Nexxt.JS

Resource	Progress
Docs: Start building with Next.js

Pandas

Resource	Progress
Datacamp: Pandas Foundations	✅
Datacamp: Pandas Joins for Spreadsheet Users	✅
Datacamp: Manipulating DataFrames with pandas	✅
Datacamp: Merging DataFrames with pandas	✅
Datacamp: Data Manipulation with pandas	✅
Datacamp: Optimizing Python Code with pandas	✅
Datacamp: Streamlined Data Ingestion with pandas	✅
Datacamp: Analyzing Marketing Campaigns with pandas	✅
Datacamp: Analyzing Police Activity with pandas	✅

PyTorch

Resource	Progress
Article: PyTorch internals	⬜
Article: Taking PyTorch For Granted	⬜
Datacamp: Introduction to Deep Learning with PyTorch	✅
Datacamp: Intermediate Deep Learning with PyTorch	⬜
Datacamp: Deep Learning for Text with PyTorch	⬜
Datacamp: Deep Learning for Images with PyTorch	⬜
Deeplizard: Neural Network Programming - Deep Learning with PyTorch	✅

ReactJS

Resource	Progress
Codecademy: Learn ReactJS: Part I	✅
Codecademy: Learn ReactJS: Part II	✅
NexxtJS: React Foundations	⬜

Spacy

Resource	Progress
Datacamp: Advanced NLP with spaCy	✅

Tensorflow & Keras

Resource	Progress
Datacamp: Introduction to TensorFlow in Python	✅
Datacamp: Deep Learning in Python	✅
Datacamp: Introduction to Deep Learning with Keras	✅
Datacamp: Advanced Deep Learning with Keras	✅
Deeplizard: Keras - Python Deep Learning Neural Network API	✅
Udacity: Intro to TensorFlow for Deep Learning	✅

VSCode

Resource	Progress
VSCode Docs: Python Interactive window	⬜

Miscellaneous

Design

Resource	Progress
Course: How to Visualize Value	✅
Article: Create an illustration in Figma design	✅
Series: K-12 Figma Design Basics	✅

Finance

Resource	Progress
Coursera: Financial Markets	⬜

Marketing

Resource	Progress
Course: Build Once, Sell Twice	✅

Search Engine Optimization (SEO)

Resource	Progress
Course: Compound Content	✅

Technical Writing

Resource	Progress
Google: Technical Writing Course	⬜
Handbook: Writing Better	⬜