Deep Learning Professional Program

Program Description

Deep Learning is one of the most highly sought after skills in Artificial Intelligence. This program 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 complete three hands-on projects and pass the Innosoft Certified Deep Learning Professional Exam (DL-300).

Course Code Dates Location Registration Fees
DL-300 31 October - 10 November 2020 Dubai Knowledge Park 2,950 AED (800 USD)

What You Will Get

KHDA-Accredited Certification

Live Classes

Lecture Videos

Practical Projects

Course Instructor - Ahmed El Koutbia

Ahmed_Passport_Photo

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).

Prerequisites

Successful Completion of our Artificial Intelligence Professional Program, or Experience with Python Programming and Machine Learning.

Course and Certification Schedule

Event Date Description Timing

Live Session 1


In-Person or via Zoom
01 November 2020
  • Meet and Greet

  • Course Overview

  • Examples of Deep Learning Projects

  • Assignment 1
7:00PM - 8:30PM

Lecture Video 1

02 November 2020
  • 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
Flexible

Lecture Video 2

03 November 2020
  • Train, Dev, Test Sets

  • Bias vs. Variance

  • Overfitting in Neural Networks

  • Early Stopping

  • Regularization (L2, Dropout)

  • Computer Vision: Convolutional Neural Networks(CNN)

  • Practical Project: Solving Tumor Classification Problem with Neural Networks

Flexible

Assignment 1 Due

04 November 2020 at 6:00PM

Assignment 2 Due

08 November 2020 at 6:00PM

Live Session 2


In-Person or via Zoom
04 November 2020
  • Q & A: Video Lectures 1 and 2

  • Assignment 1 - Solution

  • Assignment 2

7:00PM - 8:30PM

Lecture Video 3

05 November 2020
  • 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

Flexible

Lecture Video 4

07 November 2020
  • 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
Flexible

Assignment 1 Due

04 November 2020 at 6:00PM

Assignment 2 Due

08 November 2020 at 6:00PM

Live Session 3


In-Person or via Zoom
08 November 2020
  • Q & A: Lecture Videos 3 and 4

  • Assignment 2 - Solution

  • Assignment 3

7:00PM - 8:30PM

Lecture Video 5

09 November 2020
  • Building an RNN Model with TensorFlow

  • Forecasting Retail Sales with RNN

Flexible

Lecture Video 6

10 November 2020
  • Natural Language Processing (NLP) with RNN

  • Text Processing and Vectorization

  • Textual Data Preprocessing

  • Using TensorFlow to Build an NLP Model

  • Generating Sample Textual Content

Flexible

Assignment 3 Due

11 November 2020 at 6:00PM

Live Session 4


In-Person or via Zoom
11 November 2020
  • Q & A: Video Lectures 5 and 6

  • Assignment 3 - Solution

  • Certification Exam Prep

7:00PM - 8:30PM

Certification Exam (DL300)

21 November 2020

In-Class Exam


  • Dubai Knowledge Park, Block 6, Office F02


3:00PM - 5:00PM

Testimonials