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Building Machine Learning and Deep Learning Models on Google Cloud Platform

A Comprehensive Guide for Beginners

  • Ekaba ┬áBisong
Book

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Getting Started with Google Cloud Platform

    1. Front Matter
      Pages 1-1
    2. Ekaba Bisong
      Pages 3-6
    3. Ekaba Bisong
      Pages 11-24
    4. Ekaba Bisong
      Pages 25-33
    5. Ekaba Bisong
      Pages 35-48
    6. Ekaba Bisong
      Pages 49-57
    7. Ekaba Bisong
      Pages 59-64
  3. Programming Foundations for Data Science

    1. Front Matter
      Pages 65-65
    2. Ekaba Bisong
      Pages 67-70
    3. Ekaba Bisong
      Pages 71-89
    4. Ekaba Bisong
      Pages 91-113
    5. Ekaba Bisong
      Pages 115-150
    6. Ekaba Bisong
      Pages 151-165
  4. Introducing Machine Learning

    1. Front Matter
      Pages 167-167
    2. Ekaba Bisong
      Pages 169-170
    3. Ekaba Bisong
      Pages 171-197
    4. Ekaba Bisong
      Pages 199-201
    5. Ekaba Bisong
      Pages 209-211
  5. Machine Learning in Practice

    1. Front Matter
      Pages 213-213
    2. Ekaba Bisong
      Pages 215-229
    3. Ekaba Bisong
      Pages 231-241
    4. Ekaba Bisong
      Pages 243-250
    5. Ekaba Bisong
      Pages 251-254
    6. Ekaba Bisong
      Pages 255-268
    7. Ekaba Bisong
      Pages 269-286
    8. Ekaba Bisong
      Pages 309-318
    9. Ekaba Bisong
      Pages 319-324
  6. Introducing Deep Learning

    1. Front Matter
      Pages 325-325
    2. Ekaba Bisong
      Pages 327-329
    3. Ekaba Bisong
      Pages 331-332
    4. Ekaba Bisong
      Pages 333-343
  7. Deep Learning in Practice

    1. Front Matter
      Pages 345-345
    2. Ekaba Bisong
      Pages 347-399
    3. Ekaba Bisong
      Pages 401-405
    4. Ekaba Bisong
      Pages 411-413
    5. Ekaba Bisong
      Pages 415-421
    6. Ekaba Bisong
      Pages 423-441
    7. Ekaba Bisong
      Pages 443-473
    8. Ekaba Bisong
      Pages 475-482
  8. Advanced Analytics/Machine Learning on Google Cloud Platform

    1. Front Matter
      Pages 483-483
    2. Ekaba Bisong
      Pages 485-517
    3. Ekaba Bisong
      Pages 519-535
    4. Ekaba Bisong
      Pages 537-543
    5. Ekaba Bisong
      Pages 581-598

About this book

Introduction

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform.

Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments.

Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP.

You will:

  • Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results
  • Know the programming concepts relevant to machine and deep learning design and development using the Python stack
  • Build and interpret machine and deep learning models
  • Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products
  • Be aware of the different facets and design choices to consider when modeling a learning problem
  • Productionalize machine learning models into software products


Keywords

Machine learning Deep learning Google Cloud Platform Tensorflow Cloud computing Learning algorithms Intelligent machines Data Science Data analytics Predictive analytics Big Data Building big data pipelines

Authors and affiliations

  • Ekaba ┬áBisong
    • 1
  1. 1.OTTAWACanada

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