Accelerating TensorFlow with the Google Machine Learning Engine

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Created by: Matt Scarpino

Produced in 2018

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Course Description

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.

Intermediate Level

Time: 3h 5m

Instructor Details

Matt Scarpino

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Matthew Scarpino is a deeply experienced software developer specializing in high-speed software development.Currently, Matthew works as a software developer at Plutocracy.com, a company that aims to harness the power of deep learning to support individual investors and small investing firms. In his current role, he implements stock forecasting using Python, spline interpolation, and recurrent neural networks (RNNs). He has deployed TensorFlow models to the Google Cloud Machine Learning Engine for high-speed processing. Matthew's specialties include C++, Python, TensorFlow, and the Google Cloud Platform (GCP).