This course is for academic researchers to move with them step by step from scratch to advanced knowledge in the field of Deep Learning and its related topics that allow them to be able to implement their ideas and research findings. In this course, participants will learn Python and Deep Learning Neural Network from scratch then, based on a systematic learning methodology, will be able to increase their knowledge to a highly-advanced level. This intensive course is the only of its type that provides complete knowledge about almost all the cutting-edge aspects of Deep Learning, which allows the participants to be able to implement any type of related research in any area. Each participant will be worked with individually to start producing a respected project.

1. International
ATITAcademy Int’l. Amman, Jordan and Bochum, Germany. WhatsApp: +962795037290. Email: info@atitgroup.com
Course Outline
Deep Learning with Python: A Complete Practical Course for Researchers
[Online]
Mentor
Qais Yousef,
Ph.D. in Systems Optimization and Applied Neuroscience, with 10+ years of experience in
professional and academic fields.
WhatsApp: +962795037290 Email: info@atitgroup.com
Website: atitgroup.business.site Skype ID: ATITAcademy
Youtube Channel: youtube.com/c/ATITAcademy
Course Details
 Overview
This course is for academic researchers to move with them step by step from scratch to advanced
knowledge in the field of Deep Learning and its related topics that allow them to be able to implement their
ideas and research findings. In this course, participants will learn Python and Deep Learning Neural Network
from scratch then, based on a systematic learning methodology, will be able to increase their knowledge to
a highly-advanced level. This intensive course is the only of its type that provides complete knowledge about
almost all the cutting-edge aspects of Deep Learning, which allows the participants to be able to implement
any type of related research in any area. Each participant will be worked with individually to start
producing a respected project.
 Total Time
Around 33 Hours – 9 Sessions, between 3 to 4 hours long each.
Workshop Sessions
 This comprehensive course will be covered over 9 sessions and contains the below topics:
1. Introduction to Artificial Intelligence and Deep Learning
 What is Artificial Intelligence (AI)
 What is Deep Learning (DL)
 Types of DL algorithms:
 Convolution Neural Network (CNN)
 Recurrent Neural Network (RNN)
 Long Short-Term Memory (LSTM)
 Reinforcement Learning (RL) and Deep Q-Network (DQN)
 Generative Adversarial Network (GAN)
 Applications on DL

2. International
ATITAcademy Int’l. Amman, Jordan and Bochum, Germany. WhatsApp: +962795037290. Email: info@atitgroup.com
 Operations of DL
 Practical Examples
2. Introduction to Python
 Python Basics
 Installing Python
 PIP packages installer
 Python Variables
 Input and Output
 If...Then...Else
 Loops
 Collections
 Functions
 Error Handling
 Practical Project
3. Python for Deep Learning
 Data Manipulation
 Normalizing data
 Formatting data
 Important Python Packages for Image Processing and Deep Learning:
 OpenCV
 Tensorflow
 Keras
 Dlip
 Practical Project
4. Optimization
 Optimization Overview
 DL as an optimization problem
 Types of Optimizers (Teachers)
 Optimization Approach Components
 Formulating an Objective Function
 Solving a maximization problem
 Solving a minimization problem
 Producing Convergence Curve
 Practical Project on real functions
5. DNN Layers, Activation and Loss Functions
 Input Layer
 Hidden Layer:
 Convolution Layers
 Max pooling Layers
 Classification Layer
 Output Layer
 Dropout Layer

3. International
ATITAcademy Int’l. Amman, Jordan and Bochum, Germany. WhatsApp: +962795037290. Email: info@atitgroup.com
 Fully Connected Layers
 Activation Functions:
 RELU
 Sigmoid
 Softmax
 Loss Functions:
 Mean Square Error
 Cross-Entropy Loss
 Practical Project
6. Classification Problem
 Supervised Learning
 Features Classification in Details
 CNN in details
 Classification Project 1 (General Dataset Selected by Participants)
 Classification Project 2 (Medical Dataset)
7. Clustering Problem
 Unsupervised Learning
 Features Clustering in Details
 Autoencoder algorithm in Details
 Convolutional Autoencoder (Experimental)
 Clustering Project (General Dataset Selected by Participants)
8. Regression Problem
 Definition of Regression Problems
 Simple Linear Regression
 Multiple Regression
 Assessing Performance
 Ridge Regression
 Feature Selection & Lasso
 Nearest Neighbors & Kernel Regression
 Practical project on using regression, for prediction
9. Other Deep Learning Techniques
 Transfer Learning
 Fine-tuning
 Federated-learning
 Deep Reinforcement Learning (Deep Q-Learning)
 Generative Adversarial Neural Network (GANs)
 Practical Project using Related Techniques on a Problem Selected by Participants
 A complete project will be assigned for participants in each session, (aside from the session-shared
projects) to work on at home, and is required to submit it at the beginning of every session starting
from the 2nd session. The submitted assignments will be discussed in the next session with each
student individually.
 Questions and discussions are highly encouraged during the session.

4. International
ATITAcademy Int’l. Amman, Jordan and Bochum, Germany. WhatsApp: +962795037290. Email: info@atitgroup.com
Remarks
 Each participant MUST have a suitable computer with a stable internet connection.