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COURSE OVERVIEW

IT0039 : Batch Normalization
Batch Normalization
OVERVIEW
COURSE TITLE : IT0039 : Batch Normalization
COURSE DATE : Nov 17 - Nov 21 2025
DURATION : 5 Days
INSTRUCTOR : Mr. Mohamed Radwan
VENUE : Abu Dhabi, UAE
COURSE FEE : $ 5500
Register For Course Outline

Course Description

 This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops.  
 

This course is designed to provide participants with a detailed and up-to-date overview of Batch Normalization. It covers the vanishing and exploding gradient problems and the effects of poorly scaled inputs on training stability; the normalization techniques and batch normalization as an alternative to input normalization; the mathematics behind batch normalization; the batch normalization in different layers of neural networks; how batch normalization reduces covariate shift and reducing the sensitivity to learning rate selection; the generalization and reducing overfitting; and the batch normalization and activation functions. 
 
Further, the course will also discuss the batch normalization in convolutional neural networks (CNNs) and recurrent neural networks (RNNs) and transformers; the layer normalization versus batch normalization; the instance normalization and group normalization; the difference between batch normalization, weight normalization and the need for batch renormalization in certain applications; handling very small or large batch sizes; and the impact of mini-batch size on normalization statistics. 
 
 
During this interactive course, participants will learn the batch normalization in object detection models, generative adversarial networks (GANs) and reinforcement learning (RL); the impact of batch normalization in large-scale datasets including hyperparameter tuning for batch normalization; the evolution of normalization beyond batch normalization and emerging alternatives like normalizer-free networks; and handling batch normalization in real-world inference, converting batch norm layers to fixed scaling and optimizing batch norm for mobile and edge devices 

link to course overview PDF

TRAINING METHODOLOGY

This interactive training course includes the following training methodologies:

Lectures
Practical Workshops & Work Presentations
Hands-on Practical Exercises & Case Studies
Simulators (Hardware & Software) & Videos

In an unlikely event, the course instructor may modify the above training methodology for technical reasons.

VIRTUAL TRAINING (IF APPLICABLE)

If this course is delivered online as a Virtual Training, the following limitations will be applicable:

Certificates : Only soft copy certificates will be issued
Training Materials : Only soft copy materials will be issued
Training Methodology : 80% theory, 20% practical
Training Program : 4 hours per day, from 09:30 to 13:30

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