Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

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.
Table of Contents (18 chapters)
Creating ML Applications with Firebase

Generative Neural Networks

In recent times, neural networks have been used as generative models: algorithms able to replicate the distribution of data in input to then be able to generate new values starting from that distribution. Usually, an image dataset is analyzed, and we try to learn the distribution associated with the pixels of the images to produce shapes similar to the original ones. Much work is ongoing to get neural networks to create novels, articles, art, and music.

Artificial intelligence (AI) researchers are interested in generative models because they represent a springboard towards the construction of AI systems able to use raw data from the world and automatically extract knowledge. These models seem to be a way to train computers to understand the concepts without the need for researchers to teach such concepts a priori. It would be a big step forward compared...