The Visual Guide on How Neural Networks Learn from Data

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The Visual Guide on How Neural Networks Learn from Data 4.5 (128 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.

What you’ll learn

  • Understand what Neural Networks (NNs) are all about
  • Step-by-Step in-Motion NN files for you to KEEP, yes, these files are yours!
  • Adjust Templates to your requirements => SEE what’s going on inside NNs!
  • See how Neural Networks LEARN (graphic and dynamic files), follow all causes and effects!
  • Understand KEY ALGORITHMS in NN’s (Gradient Descent, Backpropagation and more)
  • Know Types of Neural Networks, Designs and Advanced Topics


  • 1. Excel and PowerPoint (Office 2010+)
  • 2. Maths (Basics)
  • 3. A Plus: Calculus and Derivatives (Optional, not required)


Big announcemen: This course has gone FREE!

Enroll now!

Course Achievements (October 2019): 

  • +2,000 Worldwide Students enrolled
  • Trophy Awards for Key Section Achievements!


Some Student Reviews are:

“Very structured and logical” (July 2018).

“Great Explanation of NN. Will definitely recommend to others.” (April 2018)

“Enlightening overview of how neural networks operate mathematically.” (March 2018).

“An  Excellent Course” (February 2018).

“The NN is a Complex topic and the instructor explains the NN very clearly. The demonstration shows how NN learns from data in a very precise way. I highly recommend this course.” (January 2018). 

“I just loved this course. The course is very well taught and is divided in easy-to-digest units.” (December 2017).


More Student Reviews:

“Highly visual. Good explaining. Easy to grasp. I can’t praise this work enough! Absolutely brilliant teaching material” (November 2017)

“excellently delivered step by step .. visually learning is very clear and easily understandable.” (November 2017)

“Very clear and straight forward. Course is a great first step to understand the structure of a

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