FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World


 Title Mathematics for Machine Learning
 Author(s) Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
 Publisher: Cambridge University Press; 1st edition (April 23, 2020); eBook (GitHub Edition)
 Permission: This PDF version is free to view and download for personal use only. Not for redistribution, resale, or use in derivative works.
 Paperback: 398 pages
 eBook: PDF (412 pages)
 Language: English
 ISBN10/ASIN: 110845514X
 ISBN13: 9781108455145
 Share This:
Book Description
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites.
About the Author(s) Marc Peter Deisenroth is DeepMind Chair in Artificial Intelligence at the Department of Computer Science, University College London.

Mathematics for CS and Machine Learning (Jean Gallier, et al.)
Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

Mathematical Introduction to Deep Learning (Arnulf Jentzen, et al)
This book aims to provide an introduction to the topic of deep learning algorithms, coverss essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures and algorithms.

Pen and Paper Exercises in Machine Learning (Michael Gutmann)
This is a collection of (mostly) penandpaper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

Machine Learning with Neural Networks (Bernhard Mehlig)
This modern and selfcontained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

Dive into Deep Learning (Aston Zhang, et al.)
This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

Deep Learning for Coders with Fastai and PyTorch
This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

Mathematics for Computer Science (Eric Lehman, et al)
This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. explores the topics of basic combinatorics, number and graph theory, logic and proof techniques.

Foundations of Machine Learning (Mehryar Mohri, et al)
This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

Understanding Machine Learning: From Theory to Algorithms
This book explains the principles behind the automated learning approach and the considerations underlying its usage. It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

The HundredPage Machine Learning Book (Andriy Burkov)
Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.
:






















