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Full Description
Unleash Google's Cloud Platform to build, train and optimize machine learning models
About This Book
• Get well versed in GCP pre-existing services to build your own smart models
• A comprehensive guide covering aspects from data processing, analyzing to building and training ML models
• A practical approach to produce your trained ML models and port them to your mobile for easy access
Who This Book Is For
This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy
What You Will Learn
• Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile
• Create, train and optimize deep learning models for various data science problems on big data
• Learn how to leverage BigQuery to explore big datasets
• Use Google's pre-trained TensorFlow models for NLP, image, video and much more
• Create models and architectures for Time series, Reinforcement Learning, and generative models
• Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications
In Detail
Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions.
This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications.
By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Style and approach
An easy-to-follow step by step guide which will help you get to the grips with real-world applications of Google Cloud Machine Learning.
Contents
Table of Contents
Setting up and Securing the Google Cloud Platform
Interacting with Google Cloud Platform
Google Cloud Storage
Querying your data with BigQuery
Transforming your data
Essential Machine Learning
Google Machine Learning APIs
Creating Machine Learning Applications with Firebase
Implementing a Feedforward network with TensorFlow and Keras
Evaluating results with TensorBoard
Optimizing your model with HyperTune
Preventing Overfitting with regularization
Beyond Feedforward networks
Time series with LSTMs
Reinforcement Learning with Tensorflow
Generative neural networks
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