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- AI Book Recs: Add These to Your Reading List | Shana Lynch - Stanford University Human -Centered Artificial Intelligence ... Our HAI community offered up the best books in AI that they’re reading.
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- Public search | DOD and ODNI/IARPA
- Academic and Scholar Search Engines and Sources | Marcus P. Zillman - Virtual Private Library
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- 24 Best (and Free) Books To Understand Machine Learning | Reashikaa Verma - KDnuggets
- Neural Networks and Deep Learning - online book | Michael A. Nielsen
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services:
- 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
- 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
- Impromptu: Amplifying Our Humanity Through AI | Reid Hoffman ... written with GPT-4
- The AI Revolution in Medicine: GPT-4 and Beyond | P. Lee, C. Goldberg, & I. Kohane
Your life does not get better by chance, it gets better by change.- Jim Rohn
AI/ML Newsletters
Materials
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Shan Carter - OpenVisConf 2018
LESSONS FROM A YEAR OF DISTILLING MACHINE LEARNING RESEARCH
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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?
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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.
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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!
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Elementary Basic - fun book to learn programming
