COURSE OVERVIEW
IT0039 : 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:
LecturesPractical 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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