Difference between revisions of "Reading Material & Glossary"

Revision as of 11:35, 2 October 2020

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!

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 ** [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
 *** [http://tanthiamhuat.files.wordpress.com/2018/04/pythondatasciencehandbook.pdf Python Data Science Handbook (PDF) | Jake VanderPlas]
+** [http://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://aitopics.org/search AITopics |  The Association for the Advancement of Artificial Intelligence (AAAI)]
 * [http://www.topbots.com/most-important-ai-research-papers-2018/ Summarized Top 2018 Papers | Mariya Yao]
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 * [http://github.com/THUNLP-MT/MT-Reading-List#10_must_reads Machine Translation Reading List | Tsinghua Natural Language Processing Group]
 * [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]
+* [http://neuralnetworksanddeeplearning.com/index.html Neural Networks and Deep Learning - online book | Michael A. Nielsen]
 * [[Natural Language Processing (NLP)]]:
 ** [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]