Difference between revisions of "Reading Material & Glossary"

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|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS  
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|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools  
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[http://www.google.com/search?q=Glossary+deep+machine+learning+ML ...Google search]
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[https://www.youtube.com/results?search_query=ai+Reading+Material+Glossary+ML+deep+machine+learning YouTube]
 +
[https://www.quora.com/search?q=ai%20Reading%20Material%20Glossary%20ML%20deep%20machine%20learning ... Quora]
 +
[https://www.google.com/search?q=ai+Reading+Material+Glossary+ML+deep+machine+learning ...Google search]
 +
[https://www.amazon.com/s/ref=sr_nr_n_5?fst=as%3Aoff&rh=n%3A156116011%2Ck%3Aartificial+intelligence+deep+learning&sort=date-desc-rank&keywords=artificial+intelligence+deep+learning&ie=UTF8&qid=1527179113&rnid=2941120011 ... Amazon search]
 +
[https://news.google.com/search?q=ai+Reading+Material+Glossary+ML+deep+machine+learning ...Google News]
 +
[https://www.bing.com/news/search?q=ai+Reading+Material+Glossary+ML+deep+machine+learning&qft=interval%3d%228%22 ...Bing News]
  
−
* [http://www.connectedpapers.com/ Connected Papers] ... explore connected papers in a visual graph
+
* [[How do I leverage Artificial Intelligence (AI)?]] ... [[Reading Material & Glossary|Reading/Glossary]] ... [[Courses & Certifications|Courses/Certs]] ... [[Education]] ... [[Help Wanted]]
−
* [http://distill.pub/ Distill] ...an academic journal dedicated to human understanding
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* [[Humor]] ... [[Writing/Publishing]] ... [[Storytelling]] ... [[AI Generated Broadcast Content|Broadcast]]  ... [[Journalism|Journalism/News]] ... [[Podcasts]] ... [[Books, Radio & Movies - Exploring Possibilities]]
 +
* [[Math for Intelligence]] ... [[Finding Paul Revere]] ... [[Social Network Analysis (SNA)]] ... [[Dot Product]] ... [[Kernel Trick]]
 +
* [https://paperswithcode.com/ Papers With Code] ... a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables
 +
* [https://www.connectedpapers.com/ Connected Papers] ... explore connected papers in a visual graph
 +
* [https://hai.stanford.edu/news/ai-book-recs-add-these-your-reading-list 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.
 +
* [https://distill.pub/ Distill] ...an academic journal dedicated to human understanding
 
* Nomenclature:  
 
* Nomenclature:  
−
** [http://developers.google.com/machine-learning/glossary/ Machine Learning Glossary | Google]
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** [https://developers.google.com/machine-learning/glossary/ Machine Learning Glossary | Google]
−
** [http://ml-cheatsheet.readthedocs.io/en/latest/index.html# Machine Learning Glossary | readthedocs.io]
+
** [https://ml-cheatsheet.readthedocs.io/en/latest/index.html# Machine Learning Glossary | readthedocs.io]
−
** [http://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence Glossary of Artificial Intelligence | Wikipedia]
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** [https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence Glossary of Artificial Intelligence | Wikipedia]
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* [[Courses & Certifications]]
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* [https://openlibrary.org/search/subjects?q=Artificial+Intelligence+&has_fulltext=true Open Library | Internet Archive] ...non-profit library of millions of free books, movies, software, music, websites, and more.
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* [[Python]]
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* [https://www.worldcat.org/search?q=su%3Aartificial+intelligence&fq=yr%3A2016..2050+%3E&qt=advanced&dblist=638 WorldCat | OCLC Online Computer Library Center, Inc.] ...world's largest network of library content and services
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** [http://blog.finxter.com/free-python-books/ 101+ Free Python Books | Christian]
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* [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]]
−
** [http://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://blog.finxter.com/free-python-books/ 101+ Free Python Books | Christian]
−
*** [http://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf Python Data Science Handbook (PDF) | Jake VanderPlas]  
+
** [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   
−
* [http://aitopics.org/search AITopics |  The Association for the Advancement of Artificial Intelligence (AAAI)]
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*** [https://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf Python Data Science Handbook (PDF) | Jake VanderPlas]
−
* [http://www.topbots.com/most-important-ai-research-papers-2018/ Summarized Top 2018 Papers | Mariya Yao]  
+
** [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]
−
* [http://www.reddit.com/ Reddit - Machine Learning Sub-reddit]
+
* [https://aitopics.org/search AITopics |  The Association for the Advancement of Artificial Intelligence (AAAI)]
−
* [http://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
+
* [https://www.topbots.com/most-important-ai-research-papers-2018/ Summarized Top 2018 Papers | Mariya Yao]  
−
* [http://publicaccess.dtic.mil/padf_public/#/simpleSearch DOD and ODNI/IARPA] public search
+
* [https://www.reddit.com/ Reddit - Machine Learning Sub-reddit]
−
* [http://whitepapers.virtualprivatelibrary.net/Scholar.pdf Academic and Scholar Search Engines and Sources | Marcus P. Zillman - Virtual Private Library]
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* [https://www.arxiv-sanity.com/ Arxiv Sanity Preserver] to accelerate research
−
* [http://github.com/THUNLP-MT/MT-Reading-List#10_must_reads Machine Translation Reading List | Tsinghua Natural Language Processing Group]
+
* [https://publicaccess.dtic.mil/padf_public/#/simpleSearch Public search | ] [[Defense|DOD and ODNI/IARPA]] 
−
* [http://www.kdnuggets.com/2020/03/24-best-free-books-understand-machine-learning.html 24 Best (and Free) Books To Understand Machine Learning | Reashikaa Verma - KDnuggets]
+
* [https://whitepapers.virtualprivatelibrary.net/Scholar.pdf Academic and Scholar Search Engines and Sources | Marcus P. Zillman - Virtual Private Library]
−
* [[Natural Language Processing (NLP)]]:
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* [https://github.com/THUNLP-MT/MT-Reading-List#10_must_reads Machine Translation Reading List | Tsinghua Natural Language Processing Group]
−
** [http://www.manning.com/books/taming-text Taming Text - How to Find, Organize, and Manipulate It | Grant S. Ingersoll, Thomas S. Morton, and Andrew L. Farris]
+
* [https://www.kdnuggets.com/2020/03/24-best-free-books-understand-machine-learning.html 24 Best (and Free) Books To Understand Machine Learning | Reashikaa Verma - KDnuggets]
−
** [http://www.nltk.org/book_1ed/ Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit | Steven Bird, Ewan Klein, and Edward Loper]
+
* [https://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen]
−
** [http://nlp.stanford.edu/fsnlp/ Foundations of Statistical Natural Language Processing | Chris Manning and Hinrich Schütze]  
+
* [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]:
 +
** [https://www.manning.com/books/taming-text Taming Text - How to Find, Organize, and Manipulate It | Grant S. Ingersoll, Thomas S. Morton, and Andrew L. Farris]
 +
** [https://www.nltk.org/book_1ed/ Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit | Steven Bird, Ewan Klein, and Edward Loper]
 +
** [https://nlp.stanford.edu/fsnlp/ Foundations of Statistical Natural Language Processing | Chris Manning and Hinrich Schütze]
 +
* [https://www.amazon.com/gp/product/1978487525/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1 The Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence | Carlos E Perez]
 +
* [https://gumroad.com/l/IHDj Artificial Intuition: The Improbable Deep Learning Revolution | Carlos E Perez]
 +
* [https://www.kdnuggets.com/2018/04/top-free-books-deep-learning.html Top 8 Free Must-Read Books on Deep Learning]
 +
** [https://www.deeplearningbook.org/ Deep Learning | Ian Goodfellow and Yoshua Bengio and Aaron Courville - MIT Press book]
 +
** [https://neuralnetworksanddeeplearning.com/ Neural Networks and Deep Learning | Michael Nielsen]
 +
* [https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html 10 Free Must-Read Books for Machine Learning and Data Science]
 +
* [https://www.kdnuggets.com/2018/04/7-books-mathematical-foundations-data-science.html Grasp Mathematical Foundations on Machine Learning and Data Science]
 +
* [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
 +
* [https://www.tensorflow.org/programmers_guide/ TensorFlow Programmer's Guide]
 +
* [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.amazon.com/AI-Revolution-Medicine-GPT-4-Beyond The AI Revolution in Medicine: GPT-4 and Beyond | P. Lee, C. Goldberg, & I. Kohane]
  
−
[http://www.amazon.com/s/ref=sr_nr_n_5?fst=as%3Aoff&rh=n%3A156116011%2Ck%3Aartificial+intelligence+deep+learning&sort=date-desc-rank&keywords=artificial+intelligence+deep+learning&ie=UTF8&qid=1527179113&rnid=2941120011 Amazon search...]
 
  
−
* [http://www.amazon.com/gp/product/1978487525/ref=oh_aui_detailpage_o00_s00?ie=UTF8&psc=1 The Deep Learning AI Playbook: Strategy for Disruptive Artificial Intelligence | Carlos E Perez]
+
<hr><center> <b><i>
−
* [http://gumroad.com/l/IHDj Artificial Intuition: The Improbable Deep Learning Revolution | Carlos E Perez]
+
 
−
* [http://www.kdnuggets.com/2018/04/top-free-books-deep-learning.html Top 8 Free Must-Read Books on Deep Learning]
+
Your life does not get better by chance, it gets better by change.</i></b>- [https://www.goodreads.com/quotes/561636-your-life-does-not-get-better-by-chance-it-gets Jim Rohn]
−
** [http://www.deeplearningbook.org/ Deep Learning | Ian Goodfellow and Yoshua Bengio and Aaron Courville - MIT Press book]
+
 
−
** [http://neuralnetworksanddeeplearning.com/ Neural Networks and Deep Learning | Michael Nielsen]
+
</center><hr>
−
* [http://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html 10 Free Must-Read Books for Machine Learning and Data Science]
+
 
−
* [http://www.kdnuggets.com/2018/04/7-books-mathematical-foundations-data-science.html Grasp Mathematical Foundations on Machine Learning and Data Science]
+
 
−
* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
+
= AI/ML Newsletters =
−
* [http://www.tensorflow.org/programmers_guide/ TensorFlow Programmer's Guide]
+
* [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]
−
* [http://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]
+
 
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= Materials =
  
 
{|<!-- T -->
 
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<youtube>jlZsgUZaIyY</youtube>
 
<youtube>jlZsgUZaIyY</youtube>
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<b>HH1
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<b>[[Creatives#Shan Carter|Shan Carter]] - OpenVisConf 2018
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</b><br>BB1
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</b><br>LESSONS FROM A YEAR OF DISTILLING MACHINE LEARNING RESEARCH
 
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<youtube>jlZsgUZaIyY</youtube>
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<youtube>m1ZYwcApCaQ</youtube>
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<b>HH2
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<b>How to read machine learning research-papers? | Applied AI Course
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</b><br>BB2
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</b><br>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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||
 
<youtube>SHTOI0KtZnU</youtube>
 
<youtube>SHTOI0KtZnU</youtube>
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<b>HH3
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<b>How to Read a Research Paper
−
</b><br>BB3
+
</b><br>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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<youtube>pQyzdwHBbqo</youtube>
 
<youtube>pQyzdwHBbqo</youtube>
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<b>HH4
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<b>Research to Code - Machine Learning tutorial
−
</b><br>BB4
+
</b><br>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!
 
|}
 
|}
 
|}<!-- B -->
 
|}<!-- B -->
  
−
=== Elementary Basic - fun book to learn programming ===
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= Elementary Basic - fun book to learn programming =
−
* [http://www.amazon.com/Elementary-Chronicled-Learning-Computer-Sherlock/dp/0394524233 Elementary Basic , as Chronicled by John H. Watson (Learning to Program Your Computer in Basic with Sherlock Holmes) | Henry F Ledgard and Andrew Singer]
+
* [https://www.amazon.com/Elementary-Chronicled-Learning-Computer-Sherlock/dp/0394524233 Elementary Basic , as Chronicled by John H. Watson (Learning to Program Your Computer in Basic with Sherlock Holmes) | Henry F Ledgard and Andrew Singer]
−
* [http://www.shift-society.org/hapop4/abstracts/lester.pdf What can a 1980s BASIC programming textbook teach us today? | Martin Lester]  
+
* [https://www.shift-society.org/hapop4/abstracts/lester.pdf What can a 1980s BASIC programming textbook teach us today? | Martin Lester]  
−
** [http://www.shift-society.org/hapop4/slides/lester.pdf ...slides | Martin Lester]
+
** [https://www.shift-society.org/hapop4/slides/lester.pdf ...slides | Martin Lester]

Latest revision as of 22:28, 5 December 2023

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


Your life does not get better by chance, it gets better by change.- Jim Rohn


AI/ML Newsletters


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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