Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine
- Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier.
- Birmingham, UK : Packt Publishing, 2018.
- Physical description
- 1 online resource (1 volume) : illustrations
- 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 Chatbots.
- (source: Nielsen Book Data)
- Publisher's summary
Unleash Google's Cloud Platform to build, train and optimize machine learning models Key Features 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 Book DescriptionGoogle 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. 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 Who this book is forThis 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.
(source: Nielsen Book Data)
- Publication date
- 9781788398879 (electronic bk.)
- 1788398874 (electronic bk.)