Max Pumperla – författare

Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You''ll be able to use Ray to structure and run machine learning programs at scale.

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You''ll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you''ll find it easy to get started.

Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR

Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You''ll be able to use Ray to structure and run machine learning programs at scale.

Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You''ll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you''ll find it easy to get started.

Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR

Get started with Ray, the open source distributed computing framework that simplifies the process of scaling compute-intensive Python workloads. With this practical book, Python programmers, data engineers, and data scientists will learn how to leverage Ray locally and spin up compute clusters. You'll be able to use Ray to structure and run machine learning programs at scale.Authors Max Pumperla, Edward Oakes, and Richard Liaw show you how to build machine learning applications with Ray. You'll understand how Ray fits into the current landscape of machine learning tools and discover how Ray continues to integrate ever more tightly with these tools. Distributed computation is hard, but by using Ray you'll find it easy to get started.Learn how to build your first distributed applications with Ray CoreConduct hyperparameter optimization with Ray TuneUse the Ray RLlib library for reinforcement learningManage distributed training with the Ray Train libraryUse Ray to perform data processing with Ray DatasetsLearn how work with Ray Clusters and serve models with Ray ServeBuild end-to-end machine learning applications with Ray AIR

SummaryDeep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you''ll use Python to build a bot and then teach it the rules of the game.Foreword by Thore Graepel, DeepMindPurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyThe ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot!About the BookDeep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you''ll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You''ll enjoy watching your bot master the game of Go, and along the way, you''ll discover how to apply your new deep learning skills to a wide range of other scenarios!What''s insideBuild and teach a self-improving game AIEnhance classical game AI systems with deep learningImplement neural networks for deep learningAbout the ReaderAll you need are basic Python skills and high school-level math. No deep learning experience required.About the AuthorMax Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo.Table of Contents

PART 1 - FOUNDATIONS

Toward deep learning: a machine-learning introductionGo as a machine-learning problemImplementing your first Go bot

PART 2 - MACHINE LEARNING AND GAME AI

Playing games with tree searchGetting started with neural networksDesigning a neural network for Go dataLearning from data: a deep-learning botDeploying bots in the wildLearning by practice: reinforcement learningReinforcement learning with policy gradientsReinforcement learning with value methodsReinforcement learning with actor-critic methods

PART 3 - GREATER THAN THE SUM OF ITS PARTS

AlphaGo: Bringing it all togetherAlphaGo Zero: Integrating tree search with reinforcement learning

Unlock the complexities of machine learning algorithms in Spark to generate useful data insights through this data analysis tutorialAbout This Book• Process and analyze big data in a distributed and scalable way• Write sophisticated Spark pipelines that incorporate elaborate extraction• Build and use regression models to predict flight delays Who This Book Is ForAre you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.What You Will Learn• Use Spark streams to cluster tweets online• Run the PageRank algorithm to compute user influence• Perform complex manipulation of DataFrames using Spark• Define Spark pipelines to compose individual data transformations• Utilize generated models for off-line/on-line prediction• Transfer the learning from an ensemble to a simpler Neural Network• Understand basic graph properties and important graph operations• Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language• Use K-means algorithm to cluster movie reviews datasetIn DetailThe purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter.This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification.Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.Style and approachThis book takes a practical approach to help you get to grips with using Spark for analytics and to implement machine learning algorithms. We'll teach you about advanced applications of machine learning through illustrative examples. These examples will equip you to harness the potential of machine learning, through Spark, in a variety of enterprise-grade systems.

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll LearnGain an understanding of the MLOps disciplineKnow the MLOps technical stack and its componentsGet familiar with the MLOps adoption strategyUnderstand feature engineering Who This Book Is ForMachine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production