TensorFlow is an open source software library for numerical computation using data flow graphs. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
This course addresses common commercial machine learning problems using Google’s TensorFlow library. It will not only help you discover what TensorFlow is and how to use it, but will also show you the unbelievable things that can be done in machine learning with the help of examples/real-world use cases. We start off with the basic installation of Tensorflow, moving on to covering the unique features of the library such as Data Flow Graphs, training, and visualization of performance with TensorBoard—all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section.
Set up basic and advanced TensorFlow installations
Deep dive into training, validating, and monitoring training performance
Set up and run cross-sectional examples using images
Create pipelines to deal with real-world input data
Be empowered to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage
You should be familier with Python and matrix math.
Who is this course intended for?
This course is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. This course is also for software developers who wish to learn deep learning by example. With a particular focus on solving deep learning problems from several real-world sources (notMNIST, CIFAR10) using TensorFlow’s unique features, no commercial domain knowledge is required to take this course. Familiarity with Python and matrix math is expected though.