BigData News Wednesday, March 28 Linear algebra, Deep learning, Funny cartoon & more…
BigData News TLDR / Table of Contents
- Deep Learning Book Series · Introduction
- This content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (…
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- The Browser of a Data Scientist
- Yet another funny cartoon about data scientists. More cartoons about data science, can be found here.This one was originally posted here. As a bonus, here i…
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- 10 Free Must-Read Books for Machine Learning and Data Science
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- This content is part of a series following the chapter 2 on linear algebra from the Deep Learning Book by Goodfellow, I., Bengio, Y., and Courville, A. (2016).
- The aim of these notebooks is to help beginners/advanced beginners to grasp linear algebra concepts underlying deep learning and machine learning.
- The goal of this series is to provide content for beginners who wants to understand enough linear algebra to be confortable with machine learning and deep learning.
- However, I think that the chapter on linear algebra from the Deep Learning book is a bit tough for beginners.
- Finally, we will see an example on how to solve a system of linear equations with the inverse matrix.
- Yet another funny cartoon about data scientists.
- More cartoons about data science, can be found here.
- With that in mind, here’s a list of 10 free machine learning and data science titles to get your spring reading started right.
- What better way to enjoy this spring weather than with some free machine learning and data science ebooks?
- The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.
- The Elements of Statistical Learning – – By Trevor Hastie, Robert Tibshirani and Jerome Friedman – – This book descibes the important ideas in these areas in a common conceptual framework.
- The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.
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