I generally spend time a good chunk of time searching for relevant resources to broaden my knowledge and learn new things. So I’ve got a habit to pool the links and store them, to keep the assortment handy. It saves a lot of time to quickly read important concepts and troubleshoot problems.
Thought of listing down some material that I keep a track of and which I felt was extremely useful :)
Good Reads
- Interesting Language Rhetorics - “Mistakes were made”, Non-Apology Apology, Weasel Word, Non-Denail Denail.
- What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text. Detailed blog on the vision behind text encoding styles.
- Docker E2E blogs by Prakhar Srivastav A topic wise clustering of docker technology.
- Embedding Projector by Google. Interactive word embedding visualizer by Google. It projects high dimensional data through different dimensionality reduction techniques.
- MLOps concepts for busy engineers: Model Serving. Overview about various model deployment strategies.
- Diversity in AI is not your problem, it’s hers. Talks about the prevailing “hers” VS “his” pronoun bias in many language technologies.
- Losing languages, losing worlds by CNN Interactives. Audio interactive news report on why language is more central to human survival than just mere communication.
- Why You Should Do NLP Beyond English by Sebastian Ruder. Outlines how NLP ≠ English only, but should be studied for all 7000+ languages spoken around the world.
- Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. This is a grammatically correct sentence in English where the word “Buffalo” has different semantic meanings as Proper Noun, Verb and Common Noun.
- Tokenizers: How machines read. A detailed blog on how tokenizers work.
- Why are GPUs well-suited to deep learning? - Quora. High-level explanation by Tim Detters on why GPU are so fast and effective.
- What is the No Free Lunch Theorem?
- Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning. Provides different levels of in-depth understanding on how GPUs and Tensor Core work.
- Choosing the right GPU for deep learning on AWS. Advice on choosing GPU instances on AWS.
- BERT Explained – A list of Frequently Asked Questions by Yashu Seth. A concise blog summarizing the important aspects related to BERT.
- Are Chess Discussions Racist? An Adversarial Hate Speech Data Set by Ashiqur R. KhudaBukhsh, et al.
- Resources to understand REST APIs - [1] [2] [3] [4] [5] [GET/POST/PUT] [WSGI]
- References blogs to understand JSON : JavaScript Object Notation - [1] [2]
Good Books
- The Language Instinct by Steven Pinker
Read Kaustubh Dhole’s summary blog on this book. - Practical Natural Language Processing by Sowmya Vajjala, et al.
Bridges the gap between NLP research and practical applications. - Linguistic Fundamentals for Natural Language Processing by Emily M. Bender
Explains core linguistic principles succinctly. - Speech and Language Processing by Dan Jurafsky & James H. Martin
Comprehensive book to understand theoretical aspects of ML/NLP - Interpretable Machine Learning by Christoph Molnar
Great read to understand Machine Learning Interpretability or Explainable AI - NLP: A Paninian Perspective by Akshar Bharat, et al.
Provides a Paninian perspective of NLP on Indian Languages. - Linguistics for the Age of AI by Marjorie McShane and Sergei Nirenburg
Extremely rich and detailed description around Natural Language Understanding in today’s age. - The Mayfield Handbook of Technical & Scientific Writing by Leslie C. Perelman, et al.
A comprehensive advice on writing scientific / technical documents.
Good Blogs
- vsupalov. Rich blogs on anything related to Docker. [Docker Learning Roadmap]
- Pratik Bhavsar
Great blogs on NLP, ML and Data Science in general. He also runs a Data Science Slack community maxpool.club that holds some great discussions! - Chris McCormick and Jay Alammar
Perhaps the best articles to understand inner workings of Language Models, Transformers, BERT, etc. - Eugene Yan
Actively blogs about ML, Career Growth & Productivity - Kaustubh Dhole
Comprehensive blogs on niche topics in NLP, ML - Rahul Agarwal
His blogs explain deep basics of Python programming and ML. - Ajit Rajasekharan
Super blogs on Deep Learning, BERT models, and Embeddings. - Sebastian Ruder
Frequently blogs on Computational Linguistics, Transfer Learning - Chip Huyen
Elaborate blogs on best engineering practices for ML in Production. - Shashank Prasanna
Deeply informative medium articles regarding AWS GPU & Cloud Computing. - Amit Chaudhary
Explains ML concepts with clean, intuitive visual diagrams. [Machine Learning Toolbox] [Resources by Amit] - Tim Dettmers
Exhaustive blogs on inner workings of GPU, neuroscience and hardware-optimization. [1]. - Martin Thoma
Writes short blogs on ML, Efficient Coding, Web. - Robert (Munro) Monarch
Blogs on depths of Linguistics, ML and Human Intelligence. - Edward Ma [NLP Progress Tracker]
Writes short blogs on all aspects of NLP Systems on [Medium]
Self Improvement Books
Since reading books is considered as ‘food for mind’, I mainly like to read books based on spiritual and self-improvement themes. Some of the really good books I have read and would highly recommend for people with similar interests are:
- ‘The monk who sold his ferrari’ - Robin Sharma
- ‘Who will cry when you die’ - Robin Sharma
- ‘Tuesdays with Morrie’ - Mitch Albom
- ‘The Secret’ - Rhonda Byrne
- ‘Rich Dad Poor Dad’ - Robert Kiyosaki
- ‘7 habits of highly effective people’ - Stephen Covey
- ‘90 minutes in heaven’ - Don Piper (with Cecil Murphy)
