Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Machine Learning Engineering
Top Free C Programming Books 🌠 - 100% Free or Open Source!
  • Title: Machine Learning Engineering
  • Author(s) Andriy Burkov
  • Publisher: True Positive Inc. (September 5, 2020) eBook (Released Drafts)
  • License(s): "read first, buy later"
  • Paperback: 310 pages
  • eBook: PDF Files
  • Language: English
  • ISBN-10: 1999579577
  • ISBN-13: 978-1999579579
  • Share This:  
`

Book Description

From the author of a world bestseller published in eleven languages, The Hundred-Page Machine Learning Book, this new book by Andriy Burkov is the most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. Andriy Burkov has a Ph.D. in AI and is the leader of a machine learning team at Gartner. This book is based on Andriy's own 15 years of experience in solving problems with AI as well as on the published experience of the industry leaders.

About the Authors
  • Andriy Burkov is a dad of two and a machine learning expert based in Quebec City, Canada. Nine years ago, he got a Ph.D. in Artificial Intelligence, and for the last six years, he's been leading a team of machine learning developers at Gartner.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • A Brief Introduction to Machine Learning for Engineers

    This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.

  • The Hundred-Page 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.

  • 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.

  • Machine Learning Yearning (Andrew Ng)

    You will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

  • Reinforcement Learning: An Introduction, Second Edition

    It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

  • Interpretable Machine Learning: Black Box Models Explainable

    This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

  • The Big Book of Machine Learning Use Cases

    This how-to reference guide provides everything you need - including code samples and notebooks - to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

  • Probabilistic Machine Learning: An Introduction (Kevin Murphy)

    This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

  • Machine Learning from Scratch (Danny Friedman)

    This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.

  • Pattern Recognition and Machine Learning (Christopher Bishop)

    This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

  • Gaussian Processes for Machine Learning (Carl E. Rasmussen)

    This book provides a long-needed systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervised-learning problem for both regression and classification.

  • 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.

  • Deep Learning (Ian Goodfellow, et al)

    Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

Book Categories
:
Other Categories
Resources and Links