Josh Patterson – författare

The Reformers viewed the gospel as not merely one thing among many in the life of a church but rather the means by which the church exists. When the gospel is rightly declared and applied to God’s people, the church becomes “a creature of the Word.” She understands, embraces, and lives out the reality of Christ’s birth, life, death, and resurrection in more than her doctrinal statement. The gospel impacts all the church is and does.Creature of the Word lays out this concept in full, first examining the rich, scripture-based beauty of a Jesus-centered church, then clearly providing practical steps toward forming a Jesus-centered church. Authors Matt Chandler, Eric Geiger, and Josh Patterson write what will become a center- ing discussion piece for those whose goal is to be part of a church that has its theology, culture, and practice completely saturated in the gospel.

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Dive into machine learning concepts in general, as well as deep learning in particularUnderstand how deep networks evolved from neural network fundamentalsExplore the major deep network architectures, including Convolutional and RecurrentLearn how to map specific deep networks to the right problemWalk through the fundamentals of tuning general neural networks and specific deep network architecturesUse vectorization techniques for different data types with DataVec, DL4J’s workflow toolLearn how to use DL4J natively on Spark and Hadoop

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.

Dive into machine learning concepts in general, as well as deep learning in particularUnderstand how deep networks evolved from neural network fundamentalsExplore the major deep network architectures, including Convolutional and RecurrentLearn how to map specific deep networks to the right problemWalk through the fundamentals of tuning general neural networks and specific deep network architecturesUse vectorization techniques for different data types with DataVec, DL4J’s workflow toolLearn how to use DL4J natively on Spark and Hadoop

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.

Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You''ll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.

Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premises Kubernetes clusterDeploy Kubeflow on Google Cloud Platform step-by-step from the command lineUse the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWSDeploy and manage Kubeflow across a network of Azure cloud data centers around the worldUse KFServing to develop and deploy machine learning models

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.

Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You''ll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.

Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premises Kubernetes clusterDeploy Kubeflow on Google Cloud Platform step-by-step from the command lineUse the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWSDeploy and manage Kubeflow across a network of Azure cloud data centers around the worldUse KFServing to develop and deploy machine learning models