Building Machine Learning and Deep Learning Models on Google Cloud Platform : A Comprehensive Guide for Beginners / by Ekaba Bisong.

Format:

Book

Author/Creator:

Bisong, Ekaba., Author.

Language:

English

Subjects (All):

Artificial intelligence.

Big data.

Artificial Intelligence.

Big Data.

Local Subjects:

Artificial Intelligence.

Big Data.

Physical Description:

1 online resource (XXIX, 709 p. 348 illus., 344 illus. in color.)

Edition:

1st ed. 2019.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2019.

System Details:

Mode of access: World Wide Web.

text file

Summary:

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.

Contents:

Part 1: Getting Started with Google Cloud Platform

Chapter 1: What Is Cloud Computing?

Chapter 2: An Overview of Google Cloud Platform Services

Chapter 3: The Google Cloud SDK and Web CLI

Chapter 4: Google Cloud Storage (GCS)

Chapter 5: Google Compute Engine (GCE)

Chapter 6: JupyterLab Notebooks

Chapter 7: Google Colaboratory

Part 2: Programming Foundations for Data Science

Chapter 8: What is Data Science?

Chapter 9: Python

Chapter 10: Numpy

Chapter 11: Pandas

Chapter 12: Matplotlib and Seaborn

Part 3: Introducing Machine Learning

Chapter 13: What Is Machine Learning?

Chapter 14: Principles of Learning

Chapter 15: Batch vs. Online Learning

Chapter 16: Optimization for Machine Learning: Gradient Descent

Chapter 17: Learning Algorithms

Part 4: Machine Learning in Practice

Chapter 18: Introduction to Scikit-learn

Chapter 19: Linear Regression

Chapter 20: Logistic Regression

Chapter 21: Regularization for Linear Models

Chapter 22: Support Vector Machines

Chapter 23: Ensemble Methods

Chapter 24: More Supervised Machine Learning Techniques with Scikit-learn

Chapter 25: Clustering

Chapter 26: Principal Components Analysis (PCA)

Part 5: Introducing Deep Learning

Chapter 27: What is Deep Learning?

Chapter 28: Neural Network Foundations

Chapter 29: Training a Neural Network

Part 6: Deep Learning in Practice

Chapter 30: TensorFlow 2.0 and Keras

Chapter 31: The Multilayer Perceptron (MLP)

Chapter 32: Other Considerations for Training the Network

Chapter 33: More on Optimization Techniques

Chapter 34: Regularization for Deep Learning

Chapter 35: Convolutional Neural Networks (CNN)

Chapter 36: Recurrent Neural Networks (RNN)

Chapter 37: Autoencoders

Part 7: Advanced Analytics/ Machine Learning on Google Cloud Platform

Chapter 38: Google BigQuery

Chapter 39: Google Cloud Dataprep

Chapter 40: Google Cloud Dataflow

Chapter 41: Google Cloud Machine Learning Engine (Cloud MLE)

Chapter 42: Google AutoML: Cloud Vision

Chapter 43: Google AutoML: Cloud Natural Language Processing

Chapter 44: Model to Predict the Critical Temperature of Superconductors

Part 8: Productionalizing Machine Learning Solutions on GCP

Chapter 45: Containers and Google Kubernetes Engine

Chapter 46: Kubeflow and Kubeflow Pipelines

Chapter 47: Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines

.

Notes:

Includes index.

Includes bibliographical references.

ISBN:

9781484244708

1484244702

OCLC:

1126570337