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Building Machine Learning and Deep Learning Models on Google Cloud Platform

Overview of attention for book
Cover of 'Building Machine Learning and Deep Learning Models on Google Cloud Platform'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 What Is Cloud Computing?
  3. Altmetric Badge
    Chapter 2 An Overview of Google Cloud Platform Services
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    Chapter 3 The Google Cloud SDK and Web CLI
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    Chapter 4 Google Cloud Storage (GCS)
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    Chapter 5 Google Compute Engine (GCE)
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    Chapter 6 JupyterLab Notebooks
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    Chapter 7 Google Colaboratory
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    Chapter 8 What Is Data Science?
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    Chapter 9 Python
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    Chapter 10 NumPy
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    Chapter 11 Pandas
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    Chapter 12 Matplotlib and Seaborn
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    Chapter 13 What Is Machine Learning?
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    Chapter 14 Principles of Learning
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    Chapter 15 Batch vs. Online Learning
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    Chapter 16 Optimization for Machine Learning: Gradient Descent
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    Chapter 17 Learning Algorithms
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    Chapter 18 Introduction to Scikit-learn
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    Chapter 19 Linear Regression
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    Chapter 20 Logistic Regression
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    Chapter 21 Regularization for Linear Models
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    Chapter 22 Support Vector Machines
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    Chapter 23 Ensemble Methods
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    Chapter 24 More Supervised Machine Learning Techniques with Scikit-learn
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    Chapter 25 Clustering
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    Chapter 26 Principal Component Analysis (PCA)
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    Chapter 27 What Is Deep Learning?
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    Chapter 28 Neural Network Foundations
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    Chapter 29 Training a Neural Network
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    Chapter 30 TensorFlow 2.0 and Keras
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    Chapter 31 The Multilayer Perceptron (MLP)
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    Chapter 32 Other Considerations for Training the Network
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    Chapter 33 More on Optimization Techniques
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    Chapter 34 Regularization for Deep Learning
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    Chapter 35 Convolutional Neural Networks (CNN)
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    Chapter 36 Recurrent Neural Networks (RNNs)
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    Chapter 37 Autoencoders
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    Chapter 38 Google BigQuery
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    Chapter 39 Google Cloud Dataprep
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    Chapter 40 Google Cloud Dataflow
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    Chapter 41 Google Cloud Machine Learning Engine (Cloud MLE)
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    Chapter 42 Google AutoML: Cloud Vision
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    Chapter 43 Google AutoML: Cloud Natural Language Processing
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    Chapter 44 Model to Predict the Critical Temperature of Superconductors
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    Chapter 45 Containers and Google Kubernetes Engine
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    Chapter 46 Kubeflow and Kubeflow Pipelines
  48. Altmetric Badge
    Chapter 47 Deploying an End-to-End Machine Learning Solution on Kubeflow Pipelines
Overall attention for this book and its chapters
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Citations

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Title
Building Machine Learning and Deep Learning Models on Google Cloud Platform
Published by
Apress, January 2020
DOI 10.1007/978-1-4842-4470-8
ISBNs
978-1-4842-4469-2, 978-1-4842-4470-8
Authors

Bisong, Ekaba

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 359 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 359 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 50 14%
Student > Master 45 13%
Researcher 23 6%
Student > Ph. D. Student 17 5%
Lecturer 16 4%
Other 42 12%
Unknown 166 46%
Readers by discipline Count As %
Computer Science 80 22%
Engineering 39 11%
Unspecified 10 3%
Agricultural and Biological Sciences 9 3%
Earth and Planetary Sciences 6 2%
Other 44 12%
Unknown 171 48%