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COURSE OVERVIEW

IT0039 : Batch Normalization
Batch Normalization
OVERVIEW
COURSE TITLE : IT0039 : Batch Normalization
COURSE DATE : Aug 03 - Aug 07 2025
DURATION : 5 Days
INSTRUCTOR : Mr. Mohamed Radwan
VENUE : Dubai, 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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