Machine Learning with TensorFlow
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 video 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.
About The Author
Shams Ul Azeem is an undergraduate of NUST Islamabad, Pakistan, in Electrical Engineering. He has a great interest in the field of computer science and has started his journey from Android Development.Now he’s pursuing his career in Machine Learning, particularly in Deep Learning, by doing medical-related freelance projects with different companies.
He was also a member of RISE lab, NUST, and has a publication in the IEEE International Conference, ROBIO as a co-author on Designing of motions for humanoid goal keeper robots.
Who is the target audience?
- This video 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. Also aimed at software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow’s unique features.