Difference between revisions of "Reading Material & Glossary"

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** [https://blog.finxter.com/free-python-books/ 101+ Free Python Books | Christian]
 
** [https://blog.finxter.com/free-python-books/ 101+ Free Python Books | Christian]
 
** [https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb Python Data Science Handbook (Jupyter notebook)] | [[Creatives#Jake VanderPlas |Jake VanderPlas]]  - O'Reilly   
 
** [https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb Python Data Science Handbook (Jupyter notebook)] | [[Creatives#Jake VanderPlas |Jake VanderPlas]]  - O'Reilly   
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*** [https://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf Python Data Science Handbook (PDF) | Jake VanderPlas]
 
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** [https://bdtechtalks.com/2020/06/03/python-machine-learning-3rd-edition-review/ Python Machine Learning: A perfect resource for intermediate AI education | Ben Dickson - TechTalks]
 
 
* [https://aitopics.org/search AITopics |  The Association for the Advancement of Artificial Intelligence (AAAI)]
 
* [https://aitopics.org/search AITopics |  The Association for the Advancement of Artificial Intelligence (AAAI)]
 
* [https://www.topbots.com/most-important-ai-research-papers-2018/ Summarized Top 2018 Papers | Mariya Yao]  
 
* [https://www.topbots.com/most-important-ai-research-papers-2018/ Summarized Top 2018 Papers | Mariya Yao]  
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* [https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1530011690&sr=8-1 Programming Collective Intelligence: Building Smart Web 2.0 Applications | Toby Segaran]
 
* [https://www.amazon.com/Programming-Collective-Intelligence-Building-Applications/dp/0596529325/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=1530011690&sr=8-1 Programming Collective Intelligence: Building Smart Web 2.0 Applications | Toby Segaran]
 
* [https://www.impromptubook.com/wp-content/uploads/2023/03/impromptu-rh.pdf Impromptu: Amplifying Our Humanity Through AI | Reid Hoffman] ... written with GPT-4
 
* [https://www.impromptubook.com/wp-content/uploads/2023/03/impromptu-rh.pdf Impromptu: Amplifying Our Humanity Through AI | Reid Hoffman] ... written with GPT-4
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* [https://www.amazon.com/AI-Revolution-Medicine-GPT-4-Beyond The AI Revolution in Medicine: GPT-4 and Beyond | P. Lee, C. Goldberg, & I. Kohane]
 
 
* [https://arxiv.org/abs/2501.00001 Scaling Laws for Reasoning-Centric Models | Research Paper - January 2026]
 
* [https://arxiv.org/abs/2501.00001 Scaling Laws for Reasoning-Centric Models | Research Paper - January 2026]
 
** Highlights the shift from pre-training scale to inference-time compute (test-time scaling).
 
** Highlights the shift from pre-training scale to inference-time compute (test-time scaling).
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* [https://openai.com/news/advancing-reasoning-capabilities-2026/ Advancing Reasoning Capabilities in Large Models | OpenAI Blog - May 2026]
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* [https://arxiv.org/abs/2501.12948 DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning | DeepSeek AI Team - arXiv] ... Breakthrough open-weights reasoning architecture and test-time compute dynamics
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* [https://deepmind.google/discover/blog/state-space-models-vs-transformers/ State-Space Models vs Transformers: The Future of Context | Google DeepMind - March 2026]
 
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* [https://huggingface.co/blog/open-source-llm-leaderboard-2026 Open Source LLM Leaderboard Update | Hugging Face - August 2026]
 
  
 
= AI/ML Newsletters =
 
= AI/ML Newsletters =
 
* [https://github.com/swyxio/ai-notes/blob/main/Resources/Good%20AI%20Podcasts%20and%20Newsletters.md A live updating list of good AI/ML Newsletters | swyx]
 
* [https://github.com/swyxio/ai-notes/blob/main/Resources/Good%20AI%20Podcasts%20and%20Newsletters.md A live updating list of good AI/ML Newsletters | swyx]
  
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= Recent Industry Coverage =
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* [https://venturebeat.com/ai/the-evolution-of-ai-documentation-in-2026/ The Evolution of AI Documentation and Knowledge Curation in 2026 | VB Staff - VentureBeat] ... How structured glossaries and live-updated reference hubs improve machine learning engineering workflows
  
 
= Materials =
 
= Materials =

Revision as of 11:08, 6 September 2026

YouTube ... Quora ...Google search ... Amazon search ...Google News ...Bing News

AI/ML Newsletters

Recent Industry Coverage

Materials

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

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