This course shows you how to install and use TensorFlow, a leading machine learning library from Google. You'll see how TensorFlow can create a range of machine learning models, from simple linear regression to complex deep neural networks.
Developing sophisticated machine learning solutions is a difficult task. There are many processing steps that must be performed, and how this processing is performed is a function of not only the code you write, but also the data you use. In this course, TensorFlow: Getting Started, you'll see how TensorFlow easily addresses these concerns by learning TensorFlow from the bottom up. First, you'll be introduced to the installation process, building simple and advanced models, and utilizing additional libraries that make development even easier. Along the way, you'll learn how the unique architecture in TensorFlow lets you perform your computing on systems as small as a Raspberry Pi, and as large as a data farm. Finally, you'll explore using TensorFlow with neural networks in general, and specifically with powerful deep neural networks. By the end of this course, you'll have a solid foundation on using TensorFlow, and have the knowledge to apply TensorFlow to create your own machine learning solutions.
Course Overview Hi my name is Jerry Kurata and welcome to my course TensorFlow: Getting Started. Every day, we see solutions utilizing machine learning based applications, from automatically adding the names of everyone in pictures posted online to diagnosing medical conditions. These systems utilize the power of machine learning and neural networks to perform tasks with amazing speed and accuracy. And TensorFlow is the premier framework for building these solutions. In this course, you'll learn how to build neural networks and other machine learning solutions with TensorFlow. This course will introduce you to TensorFlow's special architecture that lets you easily develop your solution on your laptop and scale your solution to large server farms. We start by creating simple models, then go on to learn about neural networks in TensorFlow and apply their features to creating complex solutions which identify items and pictures. Once we have a solution, we show how to deploy it and learn about the unique monitoring and debugging features provided with TensorFlow. And all of this will be easy to do with TensorFlow's expressive syntax and structures. By the end of this course, you will have a solid foundation in using TensorFlow and know how to apply TensorFlow to create your world-class machine learning solution. I look forward to you joining me on this journey of TensorFlow: Getting Started from Pluralsight.