Building Machine Learning Powered Applications Book [PDF] Download

Download the fantastic book titled Building Machine Learning Powered Applications written by Emmanuel Ameisen, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Building Machine Learning Powered Applications", which was released on 21 January 2020. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Building Machine Learning Powered Applications by Emmanuel Ameisen PDF

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers—including experienced practitioners and novices alike—will learn the tools, best practices, and challenges involved in building a real-world ML application step by step. Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies. This book will help you: Define your product goal and set up a machine learning problem Build your first end-to-end pipeline quickly and acquire an initial dataset Train and evaluate your ML models and address performance bottlenecks Deploy and monitor your models in a production environment


Detail About Building Machine Learning Powered Applications PDF

  • Author : Emmanuel Ameisen
  • Publisher : "O'Reilly Media, Inc."
  • Genre : Computers
  • Total Pages : 267 pages
  • ISBN : 1492045063
  • PDF File Size : 41,5 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

Clicking on the GET BOOK button will initiate the downloading process of Building Machine Learning Powered Applications by Emmanuel Ameisen. This book is available in ePub and PDF format with a single click unlimited downloads.

GET BOOK

Building Machine Learning Powered Applications

Building Machine Learning Powered Applications
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 54,8 Mb
  • Release Date : 21 January 2020
GET BOOK

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from

Building Machine Learning Pipelines

Building Machine Learning Pipelines
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 23,6 Mb
  • Release Date : 13 July 2020
GET BOOK

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson

Graph-Powered Machine Learning

Graph-Powered Machine Learning
  • Publisher : Simon and Schuster
  • File Size : 44,8 Mb
  • Release Date : 05 October 2021
GET BOOK

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning

Grokking Deep Learning

Grokking Deep Learning
  • Publisher : Simon and Schuster
  • File Size : 29,5 Mb
  • Release Date : 23 January 2019
GET BOOK

Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the

Machine Learning in Action

Machine Learning in Action
  • Publisher : Simon and Schuster
  • File Size : 22,8 Mb
  • Release Date : 03 April 2012
GET BOOK

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the

Building Machine Learning Powered Applications

Building Machine Learning Powered Applications
  • Publisher : Unknown Publisher
  • File Size : 25,7 Mb
  • Release Date : 19 May 2024
GET BOOK

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you'll build an example ML-driven application from initial

Building Data-Driven Applications with Danfo.js

Building Data-Driven Applications with Danfo.js
  • Publisher : Packt Publishing Ltd
  • File Size : 48,9 Mb
  • Release Date : 24 September 2021
GET BOOK

Get hands-on with building data-driven applications using Danfo.js in combination with other data analysis tools and techniques Key FeaturesBuild microservices to perform data transformation and ML model serving in

TinyML

TinyML
  • Publisher : O'Reilly Media
  • File Size : 28,8 Mb
  • Release Date : 16 December 2019
GET BOOK

Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With

Machine Learning for Business

Machine Learning for Business
  • Publisher : Simon and Schuster
  • File Size : 27,8 Mb
  • Release Date : 24 December 2019
GET BOOK

Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and

Programming Collective Intelligence

Programming Collective Intelligence
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 35,8 Mb
  • Release Date : 16 August 2007
GET BOOK

Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount