In this course, Understanding the Foundations of TensorFlow, you'll learn the TensorFlow library from very first principles. First, you'll start with the basics of machine learning using linear regression as an example and focuses on understanding fundamental concepts in TensorFlow. Next, you'll discover how to apply them to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions, and the computation graph. Then, you'll be introduced to TensorBoard, the visualization tool used to view and debug the data flow graphs. You'll work with basic math operations and image transformations to see how common computations are performed. Finally, you'll solve a real world machine learning problem using the MNIST handwritten dataset and the k-nearest-neighbours algorithm. By the end of this course, you'll have a better understanding of the foundations of TensorFlow.
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.