Deep Learning Professional Program

Program Description

Deep Learning is one of the most highly sought after skills in Artificial Intelligence. This program will enable you to build a solid foundation in Deep Learning; you will learn how to build neural networks from scratch and how to apply Convolutional Neural Networks (CNN) to solve computer vision problems such as image classification and object detection.  Moreover, you will learn how to build, and tune predictive models for sequence data, including time series and natural language, using Recurrent Neural Networks (RNN).

To complete this program successfully, the participants need to pass the Innosoft Certified Deep Learning Professional Exam (DL-300).

This program is accredited by the Knowledge and Human Development Authority (KHDA) of Dubai.

What You Will Get

In-Person and Live Training

KHDA-Accredited Certification

Practical Projects

Career Oppoturnities

Training Calendar

Description Dates Timing Location
Hybrid Training

(In-Person & Live Webinars)
6, 9, 13, 16, 20, 23 June 2023 Tuesdays & Fridays: 7:00 PM - 9:30 PM Dubai Knowledge Park
Certification (DL-300) 25 June 2023 Sundays: 11:00 AM - 1:00 PM Dubai Knowledge Park


  • Professionals or students who are interested in deep learning, and already have experience in Python Programming and Machine Learning.
  • Future Data Science Professionals and Machine Learning Engineers
  • Intermediate Python programmers interested in enhancing their existing skills.


  • Python Programming Experience
  • Basic Understanding of Machine Learning would be helpful
  • Basic Understanding of Linear Algebra, Statistics and Probabilities

Course Instructor - Ahmed El Koutbia

Founder and CEO of Innosoft Gulf, a leading AI and Big Data Education Center in Dubai. Most of the work that I do at Innosoft Gulf involves teaching, consulting and research in Deep Learning, Machine learning and Big Data. In the last couple of years, I have trained hundreds of professionals in these areas.  To develop a local community of AI and Big Data professionals, I started in March 2017, one ofthe largest meet-up groups in Dubai: Innosoft Gulf – Big Data and Artificial Intelligence. At the moment, this active meet-up group has over 3,800 members.

After completing my Bachelor’s degree in Information and Decision Science at the University of Illinois at Chicago in 1996, I had the opportunity to work for some of the most prestigious organizations in the US including Sun Microsystems and Chicago Board of Options Exchange (CBOE).  At Sun, I worked closely with the Chief Architect of the Java Center in the architecture, design and development of a Java EE based workflow engine. This work was included in the best selling  book Java EE Patterns.  Currently, I am pursuing graduate studies in AI at Stanford University, where I worked recently on a self-driving project, which applies Fully Convolutional Neural Networks for Automated Traffic Lane Detection (Click here to download the project report).

Program Syllabus

Event Date Timing Description

Session 1

In-Person & Live Webinar
Week 1 7:00 PM - 9:30 PM
  • Artificial Neural Network (ANN) Structure

  • Model Representation

  • Forward Propagation

  • Activation Functions

  • Cost and Loss Functions

  • Back Propagation and Gradient Descent

  • Practical Project: Solving a Regression Problem with Neural Networks

Session 2

In-Person & Live Webinar
Week 1 7:00 PM - 9:30 PM
  • Train, Dev, Test Sets

  • Bias vs. Variance

  • Overfitting in Neural Networks

  • Early Stopping

  • Regularization (L2, Dropout)

  • Computer Vision: Convolutional Neural Networks(CNN)

  • Practical Project: Applying Regularization to Binary Classification

Session 3

In-Person & Live Webinar
Week 2 7:00 PM - 9:30 PM
  • Introduction to Computer Vision

  • Convolution Operation and Edge Detection

  • Padding

  • Strides

  • Pooling Layers

  • Template for building a Convolutional Neural Networks

  • Overview of CNN Architectures

  • Practical Project: Developing a CNN Model for Image Classification

Session 4

In-Person & Live Webinar
Week 2 7:00 PM - 9:30 PM
  • Sequence Data

  • Recurrent Neural Network (RNN) Model

  • RNN Arcitectures

  • Vanishing Gradients

  • Long Short-Term Memory (LSTM)

  • Gated Recurrent Units (GRU)

  • Practical Project: Predicting Time Series with RNN

Session 5

In-Person & Live Webinar
Week 3 7:00 PM - 8:30 PM
  • Building an RNN Model with TensorFlow

  • Practical Project: Forecasting Retail Sales with RNN

Session 6

In-Person & Live Webinar
Week 3 7:00 PM - 9:30 PM
  • Natural Language Processing (NLP) with RNN

  • Text Processing and Vectorization

  • Textual Data Preprocessing

  • Using TensorFlow to Build an NLP Model

  • Practical Lab: Generating Shakespeare-like Poetry

Certification Exam (DL300)

Week 4 3:00PM - 5:00PM Performance Based Exam on Deep Learning