Reading Material & Glossary

...Google search

• Connected Papers ... explore connected papers in a visual graph
• Distill ...an academic journal dedicated to human understanding
• Nomenclature:
  • Machine Learning Glossary | Google
  • Machine Learning Glossary | readthedocs.io
  • Glossary of Artificial Intelligence | Wikipedia
• Open Library | Internet Archive ...non-profit library of millions of free books, movies, software, music, websites, and more.
• WorldCat ...world's largest network of library content and services
• Courses & Certifications
• Python
  • 101+ Free Python Books | Christian
  • Python Data Science Handbook (Jupyter notebook) | Jake VanderPlas - O'Reilly
    • Python Data Science Handbook (PDF) | Jake VanderPlas
• AITopics | The Association for the Advancement of Artificial Intelligence (AAAI)
• Summarized Top 2018 Papers | Mariya Yao
• Reddit - Machine Learning Sub-reddit
• Arxiv Sanity Preserver to accelerate research
• DOD and ODNI/IARPA public search
• Academic and Scholar Search Engines and Sources | Marcus P. Zillman - Virtual Private Library
• Machine Translation Reading List | Tsinghua Natural Language Processing Group
• 24 Best (and Free) Books To Understand Machine Learning | Reashikaa Verma - KDnuggets
• Natural Language Processing (NLP):
  • Taming Text - How to Find, Organize, and Manipulate It | Grant S. Ingersoll, Thomas S. Morton, and Andrew L. Farris
  • Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit | Steven Bird, Ewan Klein, and Edward Loper
  • Foundations of Statistical Natural Language Processing | Chris Manning and Hinrich Schütze

Amazon search...

• The Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence | Carlos E Perez
• Artificial Intuition: The Improbable Deep Learning Revolution | Carlos E Perez
• Top 8 Free Must-Read Books on Deep Learning
  • Deep Learning | Ian Goodfellow and Yoshua Bengio and Aaron Courville - MIT Press book
  • Neural Networks and Deep Learning | Michael Nielsen
• 10 Free Must-Read Books for Machine Learning and Data Science
• Grasp Mathematical Foundations on Machine Learning and Data Science
• Neural Network Zoo | Fjodor Van Veen
• TensorFlow Programmer's Guide
• Programming Collective Intelligence: Building Smart Web 2.0 Applications | Toby Segaran

Shan Carter - OpenVisConf 2018 LESSONS FROM A YEAR OF DISTILLING MACHINE LEARNING RESEARCH

How to read machine learning research-papers? | Applied AI Course Bianca Aguglia 1. Fast scan of the paper. 2. Careful reading. Trying to understand the logic of the paper using your current level of knowledge. 3. Working out the things not understood in step 2. 4. Question the choices made by the author of the paper. Argue with the paper. 5. Full fledged understanding. Implement the paper. Read the abstract. What problem is the paper trying to solve? Read other sources (blog posts, Wikipedia, etc) to understand the tools and techniques used in the paper. ** Mark the things you don’t understand. Decide if it’s a paper you really want to understand so you can implement it. If yes, move on to step 3. Work out the advanced math. Read other research papers. Take notes and work out the problems yourself. Explain the paper to someone else (or yourself) Are there better choices? Are there errors in the paper?

How to Read a Research Paper Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.

Research to Code - Machine Learning tutorial A lot of times, research papers don't have an associated codebase that you can browse and run yourself. In cases like that, you'll have to code up the paper yourself. That is easier said than done, and in this video i'll show you how you should read and dissect a research paper so you can quickly implement it programmatically. The paper we'll be implementing in this video is called Neural Style transfer, that applies artistic filters to an image using 3 loss functions. Its a great starting point, i'll demo it using code, animations, and math. Enjoy!

Elementary Basic - fun book to learn programming

• Elementary Basic , as Chronicled by John H. Watson (Learning to Program Your Computer in Basic with Sherlock Holmes) | Henry F Ledgard and Andrew Singer
• What can a 1980s BASIC programming textbook teach us today? | Martin Lester
  • ...slides | Martin Lester