Accelerating TensorFlow with the Google Machine Learning Engine
MP4 | Video: 720p | Duration: 3:05:37 | English | Subtitles: VTT | 350.5 MB

Discover how to leverage TensorFlowR12;an open-source software library for numerical computationR12;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.



Topics include:



Creating tensors
Basic and advanced tensor operations
Accessing graphs and sessions in code
Training TensorFlow applications
Using variables in code
Using optimizers
Using TensorBoard in practice
Accessing data with datasets
Coding with datasets and iterators
Machine learning with estimators
Deploying estimators to the Machine Learning Engine