Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform Book Cover
Hands-On Machine Learning on Google Cloud Platform
Implementing smart and efficient analytics using Cloud ML
Engine
by:Giuseppe Ciaburro, K Ayyadevara, Alexis Perrier
pages 页数:585 pages
Publisher Finelybook 出版社:Packt Publishing – ebooks Account (July 10, 2018)
Language 语言:English
ISBN-10 书号:1788393481
ISBN-13 书号:9781788393485


Book Description
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.
Contents
1:INTRODUCING THE GOOGLE CLOUD PLATFORM
2:GOOGLE COMPUTE ENGINE
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
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
Authors
Giuseppe Ciaburro
Giuseppe Ciaburro holds a PhD in environmental technical physics and two master’s degrees. His research is on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory, Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years’ experience in programming Python, R, and MATLAB, first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.
V Kishore Ayyadevara
V Kishore Ayyadevara has over 9 years’ experience of using analytics to solve business problems and setting up analytical work streams through his work at American Express, Amazon, and, more recently, a retail analytics consulting startup. He has an MBA from IIM Calcutta and is also an electronics and communications engineer. He has worked in credit risk analytics, supply chain analytics, and consulting for multiple FMCG companies to identify ways to improve their profitability.

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