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Hands-On Machine Learning on Google Cloud Platform

You're reading from   Hands-On Machine Learning on Google Cloud Platform Implementing smart and efficient analytics using Cloud ML Engine

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788393485
Length 500 pages
Edition 1st Edition
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Authors (3):
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Alexis Perrier Alexis Perrier
Author Profile Icon Alexis Perrier
Alexis Perrier
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (18) Chapters Close

Preface 1. Introducing the Google Cloud Platform 2. Google Compute Engine FREE CHAPTER 3. Google Cloud Storage 4. Querying Your Data with BigQuery 5. Transforming Your Data 6. Essential Machine Learning 7. Google Machine Learning APIs 8. Creating ML Applications with Firebase 9. Neural Networks with TensorFlow and Keras 10. Evaluating Results with TensorBoard 11. Optimizing the Model through Hyperparameter Tuning 12. Preventing Overfitting with Regularization 13. Beyond Feedforward Networks – CNN and RNN 14. Time Series with LSTMs 15. Reinforcement Learning 16. Generative Neural Networks 17. Chatbots

Overview of machine learning techniques

Before going through an overview of the major machine learning techniques, let's go through the function that we would want to optimize in a regression technique or a classification technique.

Objective function in regression

In a regression exercise, we estimate the continuous variable value. In such a scenario, our predictions can be lower than the actual value or higher; that is, the error value could be either positive or negative. In such a scenario, the objective function translates to minimizing the sum of squared values of the difference between the actual and predicted values of each of the observations in the dataset.

In mathematical terms, the preceding is written as...

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