Building Machine Learning Powered Applications: Going from...

Main
Computers - Artificial Intelligence (AI)
Building Machine Learning Powered...

Building Machine Learning Powered Applications: Going from Idea to Product

Emmanuel Ameisen

5.0 / 5.0

Koliko vam se sviđa ova knjiga?

Kakav je kvalitet fajla?

Preuzmite knjigu radi procene kvaliteta

Kakav je kvalitet preuzetih fajlova?

Kategorije:

Computers - Artificial Intelligence (AI)

Godina:

2020

Izdanje:

1

Izdavač:

O'Reilly Media

Jezik:

english

Strane:

260

ISBN 10:

149204511X

ISBN 13:

9781492045113

Fajl:

PDF, 13.97 MB

IPFS:

,

english, 2020

Čitati Online

Konvertovanje u je u toku

Konvertovanje u nije uspešno

Možda će vas zanimati

Mark Needham — Building Real-Time Analytics Systems: From Events to Insights with Apache Kafka and Apache Pinot

Micheal Lanham — Practical AI on the Google Cloud Platform

Daniel Voigt Godoy — Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Andrew Ng — Machine Learning Yearning

Pedro Domingos — The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

Moroney, Laurence — AI and Machine Learning for On-Device Development: A Programmer's Guide

Valliappa Lakshmanan, Martin Görner, Ryan Gillard — Practical Machine Learning for Computer Vision: End-to-End Machine Learning for Images

Noah Gift, Alfredo Deza — Practical MLOps: Operationalizing Machine Learning Models

Lewis Tunstall, Leandro von Werra, Thomas Wolf — Natural Language Processing with Transformers: Building Language Applications with Hugging Face

Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills — Advanced Analytics with PySpark: Patterns for Learning from Data at Scale Using Python and Spark

Cathy Chen, Niall Richard Murphy, Kranti Parisa, D. Sculley, Todd Underwood — Reliable Machine Learning: Applying SRE Principles to ML in Production

Hala Nelson — Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems

Chip Huyen — Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Third Early Release)

Chip Huyen — Designing Machine Learning Systems

Joe Reis, Matt Housley — Fundamentals of Data Engineering: Plan and Build Robust Data Systems

Michael Hausenblas — Learning Modern Linux: A Handbook for the Cloud Native Practitioner

Kyran Dale — Data Visualization with Python and JavaScript, 2nd Edition

Valliappa Lakshmanan — Machine Learning Design Patterns

Valliappa Lakshmanan — Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

Daniel Situnayake, Jenny Plunkett — AI at the Edge

Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar — Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines

Sofien Kaabar — Mastering Financial Pattern Recognition

Mike Cohen — Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python

David Foster — Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play, 2nd Edition

Kyle Gallatin, Chris Albon — Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

Yaron Haviv, Noah Gift — Implementing MLOps in the Enterprise: A Production-First Approach

Gwendolyn Stripling, Michael Abel — Low-Code AI: A Practical Project-Driven Introduction to Machine Learning

Suhas Pai — Designing Large Language Model Applications (First Early Release)

Marco Tranquillin, Valliappa Lakshmanan, Firat Tekiner — Architecting Data and Machine Learning Platforms: Enable Analytics and AI-Driven Innovation in the Cloud

Pramod Singh — Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform

Gerard Maas, Francois Garillot — Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming

Aurelien Geron [Géron, Aurélien] — Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Anirudh Koul, Siddha Ganju, Meher Kasam — Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Ankur A. Patel — Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

Eric R. Johnston; Nic Harrigan; Mercedes Gimeno-Segovia — Programming Quantum Computers: Essential Algorithms and Code Samples

Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande — Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

Joel Grus — Data Science from Scratch: First Principles with Python

Seth Weidman — Deep Learning from Scratch: Building With Python From First Principles

David Foster — Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

Denise Koessler Gosnell, Matthias Broecheler — The Practitioner’s Guide To Graph Data: Applying Graph Thinking And Graph Technologies To Solve Complex Problems

Katy Warr — Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery

Emmanuel Ameisen — Building Machine Learning Powered Applications: Going from Idea to Product

Najčešći pojmovi

Povezani Spiskovi knjiga