Lynda - Accelerating TensorFlow with the Google Machine Learning Engine

Lynda - Accelerating TensorFlow with the Google Machine Learning Engine

Discover how to leverage TensorFlow—an open-source software library for numerical computation—to build high-performing machine learning applications. In this course, instructor Matt Scarpino helps to acquaint you with this exciting tool. Here, he explores the process of developing TensorFlow applications and running them on the Google Cloud Machine Learning (ML) Engine. Matt kicks off the course by discussing TensorFlow development in detail, starting with basic tensor operations and proceeding to graphs, sessions, variables, and training. He also goes over high-level features like datasets, iterators, and estimators. Next, Matt introduces the Google Cloud Platform (GCP) and its capabilities. He shows how to create a GCP project and access it through the Cloud SDK utility. In addition, he covers Google Cloud Storage, which enables developers to upload data that can be accessed in GCP applications. To wrap up, he steps through how to deploy your TensorFlow applications to the ML Engine.

https://www.lynda.com/TensorFlow-tutorials/Accelerating-TensorFlow-Google-Machine-Learning-Engine/677176-2.html

Introduction

1. Introducing TensorFlow

2. Fundamentals of TensorFlow Development

3. Training TensorFlow Applications

4. Accessing Data with Datasets

5. Machine Learning with Estimators

6. Deploying Estimators to the Machine Learning Engine

Conclusion

Table of Contents

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