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Deep Learning

About This Course

This course is general for Deep Learning in all AI domains, although many examples comes from automotive. It develop a SOLID background of concepts that enrich the tool set → Practical AI

At the end of the course you will learn:

  • From Neuron to Deep Neural Network
  • How to train a DNN?
  • Deep Learning Design Pattern
  • Unit Objectives
  • the need to GPU programming
  • the need to frameworks
  • the difference between frameworks levels and when to use each of them
  • the main components of the frameworks
  • the basic programming steps and link to basic ML ingredients
  • Convolution as filter
  • Convolution as Template matching
  • Traditional Computer vision vs. ConvNets

  • Requirements

  • Basic calculus
  • Basic knowledge of optimization
  • Basic knowledge of linear algebra
  • Basic knowledge of probability
  • Programming: Python
  • Ubuntu Linux machine
  • GPU is nice to have

  • Course Staff

    Course Staff Image #1

    DR. Ahmad EL-SALLAB

    Ph.D. Cairo University, 2010-2014 M.Sc., Cairo University, 2007-2009 Publications and research 15+ publications in ML/DL, Speech, Comp Vision, Robotics, NLP Research gate profile: Ahmad A. Al Sallab Valeo, 2008-Now Chief of AI/DL Runs Valeo AI lab in Self driving cars Intel, 2005-2008 Lecturer at Cairo University and Information Tech. Institute (ITI)

    Frequently Asked Questions

    What is deep learning?

    Deep learning refers to artificial neural networks that are composed of many layers. It's a growing trend in ML due to some favorable results in applications where the target function is very complex and the datasets are large.

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