What are some of the best research papers books for Machine Learning?

What are some of the best research papers books for Machine Learning?

10 Best Machine Learning Textbooks that All Data Scientists…

  • Deep Learning.
  • Artificial Intelligence: A Modern Approach.
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
  • The Hundred-Page Machine Learning Book.
  • Pattern Recognition and Machine Learning.
  • Applied Predictive Modeling.

How do you read a Machine Learning research paper?

How to read one paper

  1. Don’t start from the first page and finish at last one.
  2. Read it in multiple passes.
  3. Read the title, abstracts, figures, experiments.
  4. Go through conclusion, Figures and skip the rest.
  5. Read the rest but skip the math.
  6. Read whole but skip the parts that don’t make sense.

Where can I read Machine Learning papers?

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Below are examples of resources that will assist you in your search for pertinent information.

  • The Machine Learning Subreddit.
  • The Deep Learning Subreddit.
  • PapersWithCode.
  • Top conferences such as NIPS, ICML, ICLR.
  • Research Gate.

Which book is best for machine learning beginners?

Best Machine Learning Books for Beginners

  1. Machine Learning For Absolute Beginners: A Plain English Introduction (2nd Edition)
  2. Machine Learning (in Python and R) For Dummies (1st Edition)
  3. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started (1st Edition)

Why you should read research papers?

Researchers must read papers for several reasons: to re- view them for a conference or a class, to keep current in their field, or for a literature survey of a new field. A typi- cal researcher will likely spend hundreds of hours every year reading papers.

How you should read research papers?

Step-by-Step Instructions for Reading a Primary Research Article

  • Begin by reading the introduction, not the abstract.
  • Identify the big question.
  • Summarize the background in five sentences or less.
  • Identify the specific question(s).
  • Identify the approach.
  • Read the methods section.
  • Read the results section.
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