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Building machine learning and deep learning models on Google Cloud Platform : a comprehensive guide for beginners / Ekaba Bisong.

Bisong, Ekaba, author.
2019
Q325.5 .B57 2019eb
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Title
Building machine learning and deep learning models on Google Cloud Platform : a comprehensive guide for beginners / Ekaba Bisong.
Author
Bisong, Ekaba, author.
ISBN
9781484244708 (electronic book)
1484244702 (electronic book)
9781484244692
DOI
https://doi.org/10.1007/978-1-4842-4470-8
https://doi.org/10.1007/978-1-4842-4
Published
New York : Apress, [2019]
Copyright
©2019
Language
English
Description
1 online resource : illustrations
Item Number
10.1007/978-1-4842-4470-8 doi
10.1007/978-1-4842-4
Call Number
Q325.5 .B57 2019eb
Dewey Decimal Classification
006.3/1
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.
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Includes index.
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Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 3, 2019).
Available in Other Form
Building Machine Learning and Deep Learning Models on Google Cloud Platform : A Comprehensive Guide for Beginners
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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 Mach ine 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.

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