TensorFlow is by far the most popular deep learning software package available today. This training covers all of the essentials of TensorFlow; and provides you with hands-on experience building a deep learning model using the TensorFlow library. Every line of code written during the course is analyzed to help you understand what can be a complicated process.
The lessons look at the key mathematical foundations of deep learning models, giving you insight into what makes these techniques work. Created for software engineers and budding data scientists, the course requires basic familiarity with Python programming; as well as statistics concepts such as linear and logistic regression, machine learning concepts like classification, and linear algebra. Jupyter Notebook is used to write and run code.
- Learn how to set up TensorFlow on your machine
- Master the ability to build a deep learning model that solves true-to-life problems
- Understand how to create and run a TensorFlow graph
- Understand the benefits of deep learning compared to traditional machine learning and when it should be used
- Become familiar with the “gotchas” of the TensorFlow library
- Learn how to debug programs when things go wrong
Lucas Adams is a senior level machine learning engineer at Jet.com, where he deploys TensorFlow for computer vision and natural language processing systems. A user and contributor to TensorFlow since its release in November 2015, Lucas holds a degree in Applied Mathematics from Brown University.
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Learn Introduction to Deep Learning Models With TensorFlow from a professional trainer on your own time at your own desk.
This visual training method offers users increased retention and accelerated learning.
Breaks even the most complex applications down into simplistic steps
Comes with Extensive Working Files