Recently, several types of artificial neural networks (ANNs) have been applied to classify a specific dataset. However, most of these models use only a limited number of features as input, in which case there may not be enough information to make the prediction due to the complexity of the starting dataset. If you have more features, the run time of training would be increased and generalization performance would deteriorate due to the curse of dimesionality. In these cases, a tool to extract the characteristics would be particularly useful. RBM is a machine learning tool with a strong representation power, which is often used as a feature extractor in a wide variety of classification problems.
Hands-On Machine Learning on Google Cloud Platform
By :
Hands-On Machine Learning on Google Cloud Platform
By:
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)
Preface
Free Chapter
Introducing the Google Cloud Platform
Google Compute Engine
Google Cloud Storage
Querying Your Data with BigQuery
Transforming Your Data
Essential Machine Learning
Google Machine Learning APIs
Creating ML Applications with Firebase
Neural Networks with TensorFlow and Keras
Evaluating Results with TensorBoard
Optimizing the Model through Hyperparameter Tuning
Preventing Overfitting with Regularization
Beyond Feedforward Networks – CNN and RNN
Time Series with LSTMs
Reinforcement Learning
Generative Neural Networks
Customer Reviews