Difference between revisions of "Reading Material & Glossary"
m |
m |
||
| Line 56: | Line 56: | ||
{| class="wikitable" style="width: 550px;" | {| class="wikitable" style="width: 550px;" | ||
|| | || | ||
| − | <youtube> | + | <youtube>m1ZYwcApCaQ</youtube> |
| − | <b> | + | <b>How to read machine learning research-papers? | Applied AI Course |
| − | </b><br> | + | </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? |
|} | |} | ||
|}<!-- B --> | |}<!-- B --> | ||
Revision as of 12:06, 31 August 2020
- Connected Papers ... explore connected papers in a visual graph
- Distill ...an academic journal dedicated to human understanding
- Nomenclature:
- Courses & Certifications
- Python
- 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
- 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
|
|
|
|
