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.
Course Overview Hi, my name is Janani Ravi, and welcome to this course on Understanding the Foundations of Tensorflow. I'll introduce myself. I have a Masters in Electrical Engineering from Stanford, I have worked with companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real time collaborative editing on Google Docs, and I hold four patents for it's underlying technologies. I currently work on my own start-up, Looneycorn, a studio for high quality video content. Tensorflow is slowly growing to be the most popular library for building and deploying machine learning algorithms. This course features the tenents of flow programming language, from very first principles. It starts off with the basics of machine learning, using linear regression as an example, and focuses on understanding fundamentals concepts in Tensorflow and how they apply to machine learning. The concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, place holders, sessions, and the computation draft. The course also introduces TensorBoard, the visualization tool used to view and debug data flow graphs. You'll work with basic math operations and image transformations. You see how common computations are performed. Finally, at the end of this course you will solve a real world machine learning problem, using the M-ness handwritten data set, and the key nearest neighbors algorithm. There are demos for every module to give you hands on experiences with the Tensorflow icon user libraries, for all concepts used in this course. Experience the magic of machine learning by getting started with Tensorflow.