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

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	⬜
Datacamp: Unit Testing for Data Science in Python	✅
Book: Python 201	⬜
Book: Writing Idiomatic Python 3	⬜
Book: Test Driven Development with Python	⬜
Article: Python's many command-line utilities	⬜
Article: A Programmer’s Introduction to Unicode	⬜
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?	✅

Data Structures and Algorithms

Resource	Progress
Book: Grokking Algorithms	✅
Book: The Tech Resume Inside Out	✅
Neetcode: Algorithms and Data Structures for Beginners	✅
Udacity: Intro to Data Structures and Algorithms	✅

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	✅

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	✅

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 PostgreSQL	⬜
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	⬜

Backend Engineering

Resource	Progress
Udacity: Authentication & Authorization: OAuth	⬜
Udacity: HTTP & Web Servers	⬜
Udacity: Client-Server Communication	⬜
Udacity: Designing RESTful APIs	⬜
Datacamp: Introduction to APIs in Python	⬜
Udacity: Networking for Web Developers	⬜

Production 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	✅

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	⬜

Basic Frontend Knowledge

---

HTML

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

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	✅
Article: Create an illustration in Figma design	✅
Book: Refactoring UI	⬜
Youtube: How to Make Your Website Not Ugly: Basic UX for Programmers	⬜

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	✅

Specialized Topics

---

Machine Learning

Resource	Progress
Article: An overview of gradient descent optimization algorithms	✅
Book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition	⬜
Book: A Machine Learning Primer	✅
Book: Make Your Own Neural Network	✅
Book: Grokking Machine Learning	✅
Book: The StatQuest Illustrated Guide To Machine Learning	✅
Fast.ai: Practical Deep Learning for Coder (Part 1)	✅
Fast.ai: Practical Deep Learning for Coder (Part 2)	⬜
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: Convolutional Neural Networks for Image Processing	✅
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	✅
Karpathy: Neural Networks: Zero to Hero	✅
Article: Weight Initialization in Neural Networks: A Journey From the Basics to Kaiming	⬜

Natural Language Processing

Resource	Progress
Book: Natural Language Processing with Transformers	✅
Stanford CS224U: Natural Language Understanding | Spring 2019	✅
Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019	✅
CMU: Low-resource NLP Bootcamp 2020	✅
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: Mamba Explained	⬜
Article: A Visual Guide to Mamba and State Space Models	⬜
Article: Quantization Fundamentals with Hugging Face	✅

Generative AI

---

LLM Theory

Resource	Progress
Article: SolidGoldMagikarp (plus, prompt generation)	⬜
DeepLearning.AI: Pretraining LLMs	✅
DeepLearning.AI: How Diffusion Models 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]	✅
Youtube: 5 Years of GPTs with Finbarr Timbers	⬜
Article: Sampling for Text Generation	⬜
DeepLearning.AI: Reinforcement Learning from Human Feedback	✅
Youtube: LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU [1h10m]	⬜

Information Retrieval / RAG

Resource	Progress
Pretrained Transformer Language Models for Search - part 1	⬜
Pretrained Transformer Language Models for Search - part 2	⬜
Pretrained Transformer Language Models for Search - part 3	⬜
Pretrained Transformer Language Models for Search - part 4	⬜
Understanding LanceDB's IVF-PQ index	⬜
A little pooling goes a long way for multi-vector representations	✅
Fullstack Retrieval Course
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	⬜
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: Building Multimodal Search and RAG	⬜
DeepLearning.AI: Knowledge Graphs for RAG	⬜
DeepLearning.AI: Functions, Tools and Agents with LangChain	⬜
DeepLearning.AI: Building Agentic RAG with LlamaIndex	⬜
DeepLearning.AI: Multi AI Agent Systems with crewAI	⬜
DeepLearning.AI: AI Agentic Design Patterns with AutoGen	⬜
DeepLearning.AI: AI Agents in LangGraph	⬜
DeepLearning.AI: Building Your Own Database Agent	⬜
DeepLearning.AI: Preprocessing Unstructured Data for LLM Applications	⬜
DeepLearning.AI: Embedding Models: From Architecture to Implementation	✅
Pinecone: Vector Databases in Production for Busy Engineers	⬜
Pinecone: Retrieval Augmented Generation	⬜
Pinecone: LangChain AI Handbook	⬜
Pinecone: Embedding Methods for Image Search	⬜
Pinecone: Faiss: The Missing Manual	⬜
Pinecone: Vector Search in the Wild	⬜
Pinecone: Natural Language Processing for Semantic Search	⬜
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	0/14
Article: LambdaMART in Depth	⬜
Article: Guided Generation with Outlines	✅

Prompt Engineering

Resource	Progress
Article: OpenAI Prompt Engineering	⬜
Article: Prompting Fundamentals and How to Apply them Effectively	✅
Anthropic Courses	⬜
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: GPT-4 Vision Alternatives	⬜
DeepLearning.AI: ChatGPT Prompt Engineering for Developers	⬜
DeepLearning.AI: Prompt Engineering for Vision Models	⬜
DeepLearning.AI: Prompt Engineering with Llama 2 & 3	⬜
Wandb: LLM Engineering: Structured Outputs	⬜
DeepLearning.AI: Function-Calling and Data Extraction with LLMs	⬜
Series: Prompt injection	⬜
Youtube: Prompt Engineering Overview [1hr4m]	✅
Youtube: Structured Generation with LLMs	⬜

LLMOps

Resource	Progress
Article: Patterns for Building LLM-based Systems & Products	✅
Article: Emerging Architectures for LLM Applications	✅
Article: How to make LLMs go fast	⬜
Article: In the Fast Lane! Speculative Decoding - 10x Larger Model, No Extra Cost	⬜
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	✅
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	⬜
DeepLearning.AI: LLMOps	⬜
DeepLearning.AI: Serverless LLM apps with Amazon Bedrock	⬜
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	⬜
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	⬜
Youtube: SBTB 2023: Charles Frye, Parallel Processors: Past & Future Connections Between LLMs and OS Kernels	⬜
Article: Transformer Inference Arithmetic	⬜

Building LLM-based Systems

Resource	Progress
Article: What We’ve Learned From A Year of Building with LLMs	⬜
Article: How to Generate and Use Synthetic Data for Finetuning	✅
Article: Your AI Product Needs Evals	✅
Article: Task-Specific LLM Evals that Do & Don't Work	✅
Article: Data Flywheels for LLM Applications	⬜
Article: LLM From the Trenches: 10 Lessons Learned Operationalizing Models at GoDaddy	✅
Article: Evaluation & Hallucination Detection for Abstractive Summaries	✅
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	⬜
DeepLearning.AI: Finetuning Large Language Models	✅
DeepLearning.AI: Building Systems with the ChatGPT API	⬜
DeepLearning.AI: LangChain for LLM Application Development	⬜
DeepLearning.AI: LangChain: Chat with Your Data	⬜
DeepLearning.AI: Building Generative AI Applications with Gradio	✅
DeepLearning.AI: Open Source Models with Hugging Face	⬜
DeepLearning.AI: Getting Started with Mistral	⬜
Datacamp: Developing LLM Applications with LangChain	⬜
LLMOps: Building with LLMs	⬜
LLM Bootcamp - Spring 2023	✅
Youtube: A Survey of Techniques for Maximizing LLM Performance	✅
Youtube: Building Blocks for LLM Systems & Products: Eugene Yan	✅
Youtube: Fine Tuning OpenAI Models - Best Practices	✅
Youtube: Course: LLM Fine-Tuning w/Axolotl	0/4
Youtube: Fine-Tuning LLMs	1/5
Youtube: LLM Evals	0/5
Youtube: Building LLM Applications	0/8

Technical Skills (Libraries/Frameworks/Tools)

AWS

Resource	Progress
Udemy: AWS Certified Developer - Associate 2018	✅

Django

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

Matplotlib

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

MLFlow

Resource	Progress
Datacamp: Introduction to MLFlow	✅

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	✅