Machine Learning and Data Mining
- A long introduction to Machine Learning
- Understanding Generalisation in Machine Learning
- Strategies for preventing Model overfitting
- On Curse of Dimensionality
- Collaborative Filtering Tutorial
- What is the bias-variance tradeoff?
Reinforcement Learning
- Three Pillers of Reinforcement Learning
- Beyond simulation, applying RL in real world: Challenges and Opportunities
- Counter-factual Policy Evaluation
- Understanding Explore vs Exploit Dilemma in Online decision-making
Data Analysis and Data Engineering:
- Four common data science concepts
- Tips for Exploratory Data Analysis
- Array Programming with Numpy
- Getting Started with Data Engineering in Spark
- Exploring data via Command-Line
Signal Processing:
- Removing Image Compression Artifacts
- The Two-Dimensional Fourier Transform and Digital Watermarking
- History of Deep Learning
Natural Language
- Latent Semantic Indexing with Spark MLlib
- Transfer Learning in NLP
- Getting started with Data Engineering in Spark
Programming/Software Engineering:
- Elaborating Scalability Requirements of Large Scale Systems
- General guide on improving software engineering skills
- Brief Survey of Automated Software Testing Tools
- Tips and Quotes on becoming a better programming (long)
- Big ideas in Computer Science
- Combing Boolean satisfiability and Adversarial Search
- Understanding AlphaGo Fundamentals - Monte-Carlo Tree Search (part 2)
- Understanding AlphaGo Fundamentals - Min-Max Game tree search (part 1)
- Planning in AI
- Getting Started with LISP
- Maze Solver agent in Prolog
- Stateful dataflow graphs in tensorflow
- Solving Traveling Salesmen with Genetic Algorithms
Mini-Books:
These based on my old learning notes that I am slowly converting into mini-books with help of codex.
- Dynamic decision Making and Reinforcement Learning
- Big ideas in Deep Learning
- Software Engineering
- MLOPS: Best practices for Designing scalable and reliable Machine learning system