Tackle common machine learning problems with Google’s TensorFlow library and build deployable solutions
About This Video
Use raw real-world data to create pipelines to train and apply classifiers using TenserFlow
Productionize challenges and solutions
Go through a full lifecycle of a TensorFlow solution with a practical demonstration ofsystem setup, training, validation, and creating pipelines for the real world
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.
CNN Versus Fully Connected Network Performance 02m 08s
Machine Learning with TensorFlow
Shams Ul Aseem
1 hour 14 minutes
Shams Ul Aseem
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.