Machine Learning Design Patterns

This book PDF is perfect for those who love Computers genre, written by Valliappa Lakshmanan and published by O'Reilly Media which was released on 15 October 2020 with total hardcover pages 408. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Machine Learning Design Patterns books below.

Machine Learning Design Patterns
Author : Valliappa Lakshmanan
File Size : 41,8 Mb
Publisher : O'Reilly Media
Language : English
Release Date : 15 October 2020
ISBN : 9781098115753
Pages : 408 pages
Get Book

Machine Learning Design Patterns by Valliappa Lakshmanan Book PDF Summary

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly

Machine Learning Design Patterns

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.

Get Book
Deep Learning Patterns and Practices

Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production. In Deep Learning Patterns and Practices you will learn: Internal functioning of modern convolutional neural networks Procedural reuse design pattern for CNN architectures Models for mobile and IoT devices Assembling large-scale

Get Book
Distributed Machine Learning Patterns

Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges

Get Book
Applying UML and Patterns

Download or read online Applying UML and Patterns written by Craig Larman, published by Pearson Deutschland GmbH which was released on 2005. Get Applying UML and Patterns Books now! Available in PDF, ePub and Kindle.

Get Book
Introducing MLOps

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This

Get Book
Patterns  Predictions  and Actions  Foundations of Machine Learning

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin

Get Book
Holub on Patterns

* Allen Holub is a highly regarded instructor for the University of California, Berkeley, Extension. He has taught since 1982 on various topics, including Object-Oriented Analysis and Design, Java, C++, C. Holub will use this book in his Berkeley Extension classes. * Holub is a regular presenter at the Software Development conferences and

Get Book
Building Intelligent Systems

Produce a fully functioning Intelligent System that leverages machine learning and data from user interactions to improve over time and achieve success. This book teaches you how to build an Intelligent System from end to end and leverage machine learning in practice. You will understand how to apply your existing

Get Book