Information Technology Institute: DL01 Deep Learning

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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

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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