Understanding the Foundations of TensorFlow
Pluralsight
Course Summary
This course introduces TensorFlow, an open source data flow library for numerical computations using data flow graphs.
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Course Description
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.
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Course Syllabus
Course Overview- 1m 57s
—Course Overview 1m 57sIntroducing TensorFlow- 33m 58s
—Prerequisites and Course Overview 3m 28s
—Traditional ML Algorithms 8m 23s
—Representation ML Algorithms 2m 18s
—Deep Learning and Neural Networks 4m 14s
—Introducing TensorFlow 4m 18s
—The World as a Graph 2m 57s
—Downloading and Installing TensorFlow 8m 17sIntroducing Computation Graphs- 34m 36sDigging Deeper into Fundamentals- 41m 53sWorking with Images- 26m 2sSolving Basic Math Functions- 26m 13s