Hands-On Machine Learning on Google Cloud Platform

Implementing smart and efficient analytics using Cloud ML Engine

Giuseppe Ciaburro , V Kishore Ayyadevara , Alexis Perrier 지음

Packt(GCO Science) 2018년 04월 30일 출간

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▶TABLE of 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

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▶Editorial Review
Google Cloud ML Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn how to build and train different complexities of machine learning models at scale, but also to 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 how to create powerful machine-learningbased applications from scratch 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 others. We will implement a wide variety of deep learning use cases and will 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 be aware of the main difficulties that you may encounter, and be familiar with appropriate strategies to overcome these difficulties and build efficient systems.

작가정보

저자(글) 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, Universit？ 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.

저자(글) 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.

저자(글) Alexis Perrier

Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Universit？ Pierre et Marie Curie Paris VI and a PhD in signal processing from T？l？com ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.