Tensorflow is Google’s popular offering for machine e-learning and deep e-learning. It has quickly becous a popular choice of tool for performing fast, efficient, and accurate deep e-learning. This e-course presents the implementation of practical, real-world projects, teaching you how to leverage Tensforflow’s capabilties to perform efficient deep e-learning.
In this video, you will be acquainted with the different paradigms of performing deep e-learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using Tensorflow.
This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, fraud detection, and more. By the end of this e-course, you will have mastered all the concepts of deep e-learning and their implementation with Tensorflow and Keras.
About The Author
Will Ballard serves as Chief Technology Officer at GLG and is responsible for the Engineering and web organizations.
Prior to joining GLG, Will was the Executive Vice President of Technology and Engineering at Demand Media. Before that, he was Vice President and Chief Technology Officer of Pluck, through its acquisition by Demand Media. At both organizations, Will managed large teams of engineers responsible for software architecture, design, development, and quality assurance.
He was also responsible for the design and operation of large data centers that helped run site services for custousrs including Gannett, Hearst Magazines, NFL com, NPR, The Washington Post, and Whole Foods. Will has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works com (now Bank of Ausrica).
Will graduated Magna Cum Laude with a BS in Mathematics from Claremont McKenna College.
Who is the target audience?
- This e-course is for application developers looking to integrate machine e-learning into application software and master deep e-learning by implementing practical projects in Tensorflow.