Who Is This Course For ?

If you’re looking for a shallow, lightweight course, this isn’t the right fit. Our curriculum is designed for those who want to dive into the foundations of Deep Learning and apply sophisticated concepts to real-world challenges. Many of our participants are PhDs or experienced engineers, reflecting the rigorous and scientific nature of the material.

In this course, we don't take anything for granted and build knowledge from the ground up. It is perfect for professionals and researchers from other scientific domains eager to build a robust foundation in Deep Learning. If you’re a critical thinker seeking a solid, comprehensive learning experience that pushes beyond the basics, then this is the course for you.

Course Content

    1. Table of Content

      FREE PREVIEW
    2. Backpropagation

    3. Derivation by Hand

    4. Chain Rule of Calculus

    5. Computational Graphs

    6. Fully Connected Networks

    7. Data Separation through Non-Linearity

    8. Universal Approximation Theorem

    9. Parameters Norm Penalty

    10. Dropout Derivation

    11. Parameters Initialization

    12. Practical Setup

    13. Hands-On: Gender Classification From Scratch

    14. Introduction

    15. DataLoader

    16. Forward Propagation

    17. Backpropagation

    18. ADAM & LR Schedule

    19. Training & Testing

    20. Deep Learning Questions

    1. Table of Content

      FREE PREVIEW
    2. Convolution

    3. Image Processing

    4. Convolution in ML

    5. Convolution vs Dense Layers

    6. Convolutional Neural Networks

    7. Convolutional Networks

    8. Residual Connections

    9. Batch Normalization

    10. Transfer & Multi-Task Learning

    11. Fully Convolutional Networks

    12. Receptive Field

    13. Encoder Decoder Architecture

    14. Tricks for Efficient Design

    15. UNet Architecture

    1. Table of Content

      FREE PREVIEW
    2. Object Detection

    3. Introduction

    4. IoU & NMS

    5. Accuracy Metric

    6. Precision & Recall

    7. PR Curve & mAP

    8. RCNN, Fast RCNN & Faster RCNN

    9. YOLO v1 to v4

    10. Augmentation, Loss Functions & Debugging

    11. Data Augmentation Use Cases

    12. Loss Functions

    13. L1, L2 & Huber Loss

    14. Focal Loss

    15. Contrastive Loss

    16. Debugging

    17. Learning Curves

    18. Stability Issues

    19. Grad CAM

    20. Memory & Speed

    21. Hands-On: Semantic Segmentation with TF

    22. Computer Vision Questions

About this course

  • €49,99
  • Text lessons
  • Arabic video content
  • A community to ask questions
  • Lesson 1
    Linear Regression
    Learn how to formulate the problem mathematically and solve the normal equation.
  • Lesson 2
    Linear Classification
    Understand probabilistic modelling, maximum likelihood principle and derive the main loss functions.
  • Lesson 3
    Gradient Descent
    Derive gradient descent formula and understand the difference between optimization vs learning.

    Exercise: Linear models from scratch vs sklearn.
  • Lesson 4
    Backpropagation
    Discover how backpropagation made deep learning possible and which problems did it solve.
  • Lesson 5
    Fully Connected Networks
    Visualize how activation functions separate the data, what is model capacity and how to initialize the weights.
  • Hands-on 1
    Gender Classification
    Master Python: write a clean code for data loaders, neural networks and ADAM from scratch.
  • Lesson 6
    Convolution
    Understand basic image processing and how it can be implemented with convolution.
  • Lesson 7
    Convolution Neural Networks
    Replace dense layers with convolutions and design deep networks with batch norm and residual connections.

    Exercise: CNN for gender classification.
  • Lesson 8
    Fully Convolution Networks
    Understand the notion of receptive field to design powerful image-to-image networks.
  • Lesson 9
    Object Detection
    Explore the full history of object detection algorithms: RCNN vs YOLO.
  • Lesson 10
    Augmentation, Loss functions & Debugging
    Explore self-supervised learning using data augmentation and exotic loss functions and master debugging.
  • Hands-on 2
    Image Segmentation
    Master TensorFlow: implement an industrial code, explore different segmentation tasks and design a powerful UNet

Benefit from a complete package!

  • Text Lessons

    To easily take notes and summaries

  • Arabic Videos

    15 hours of intuitive explanation

  • Interview Simulation

    To be always ready and prepared

  • Practical Hands-On

    To master Python & TensorFlow

  • Community Support

    To keep the motivation on fire 🔥

  • Lifetime Access

    Get back as much as you want

Still hesitating? Don't take my words for it

5 star rating

Highly Recommended Learning Journey

Safaa ALMOKDAD

I have not yet finished the course; however, I am happy with what I have learned so far. Issa explains the most challenging concepts in an easy-to-understand manner. You can follow along with a textbook to take notes. One notable aspect is that...

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I have not yet finished the course; however, I am happy with what I have learned so far. Issa explains the most challenging concepts in an easy-to-understand manner. You can follow along with a textbook to take notes. One notable aspect is that he explains Deep Learning, starting from the very first step in DL to the last one. Although I have been enrolled in a machine learning program before, I always lacked knowledge of DL as Issa presents in his course. While following the course, you will discover and understand some of the things that were ambiguous to you. The course includes quizzes that you can take to test your knowledge and hands-on exercises to apply what you have learned so far. Another interesting aspect is the application in this course; it focuses on working with images. By the end, you will gain experience in computer vision, presented by someone who has worked extensively in this field. The spoken language of the course is Arabic, so it's a plus point for the Arabic community."

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5 star rating

A Highly Recommended Blend of Theory and Practical Expertise

Ali TFAILY

Issa's course, in my opinion, is exceptional, especially for Arabic speakers. It represents the essence of years of concentrated expertise, condensing key concepts that Issa skillfully presents to streamline the learning path for students, offeri...

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Issa's course, in my opinion, is exceptional, especially for Arabic speakers. It represents the essence of years of concentrated expertise, condensing key concepts that Issa skillfully presents to streamline the learning path for students, offering what can be challenging to find in various courses today. Notably, alongside well-explained scientific concepts, this course incorporates valuable insights derived from Issa's experience, including work on previous projects. This distinctive feature makes the course stand out. I believe that with its blend of ideas and practical applications, this course will provide a much deeper understanding to those who study it, surpassing their prior knowledge. I highly recommend it!

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5 star rating

A Great Start in the World of Deep Learning

Hussien Ismail

I came across this course as an attempt to enter into the world of deep learning and integrate it into the field ofstructural design and topology optimization. I started with zero knowledge about the topic, and now I have the solid foundation need...

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I came across this course as an attempt to enter into the world of deep learning and integrate it into the field ofstructural design and topology optimization. I started with zero knowledge about the topic, and now I have the solid foundation needed to apply what I've learned to my field. The best part of the course is that Eng. Issa explains the content as if "I am 5 years old," making it the perfect way to absorb and remember each topic. I highly recommend this course, especially for anyone willing to pursue multidisciplinary research that integrates deep learning.

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Who is behind the course?

Issa Hammoud

Senior Machine Learning Scientist

I am a Senior Machine Learning Scientist and University Instructor with several years of experience. I have worked in France with startups and big companies, in R&D and production, to solve real world problems. I like to understand the intuition behind science, and I have a good taste of coding.

Frequently Asked Questions

Reach out to us at [email protected] for any specific inquiries.

  • What sets this course apart from other online courses?

    Unlike many other courses that offer lightweight content, this course provides a comprehensive summary of key books and papers. We delve into the reasoning behind techniques, present mathematical derivations, and use engaging animations—all designed to help you grasp the underlying intuition.

  • Is this course only for Arabic speakers?

    While the video content is currently only in Arabic, everything else, including text lessons, hands-on activities, interview simulations, and community access, is in English. So, you can still benefit from the course even if you are an English speaker.

  • What resources were used to create this course?

    This course was primarily inspired by Ian Goodfellow's book "Deep Learning" for foundational concepts, various original research papers, and my own professional experience.

  • What is the estimated time to complete this course?

    Depending on your level and consistency, the course should take between 1 and 2 months to complete. We've designed it to be compact to maximize your efficiency, allowing you to gain knowledge that will benefit your professional life in a relatively short time frame.