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COURSE OVERVIEW

IT0008 : AI Multilayer Perceptron
AI Multilayer Perceptron
OVERVIEW
COURSE TITLE : IT0008 : AI Multilayer Perceptron
COURSE DATE : May 04 - May 08 2025
DURATION : 5 Days
INSTRUCTOR : Mr. Tuncay Ercan
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 Artificial Intelligence Multilayer Perceptron. It covers the artificial intelligence, multilayer perceptron and the fundamentals of artificial (ANN); the multilayer perceptron (MLP), activation functions and forward propagation in MLP; the development environment, backpropagation, cost function and optimization; the hyperparameter tuning in MLP and the causes of overfitting in neural networks; and the regularization techniques, data augmentation to reduce overfitting and cross-validation strategies. 
 
Further, the course will also discuss the performance of training and evaluating an MLP model; implementing MLP in TensorFlow and Keras and the deep MLP architectures; selecting important selecting features for MLP, handling categorical variables in MLP and data preprocessing best practices; the MLP for classification tasks, regression tasks and real-world applications; and using MLP for time-series forecasting. 

During this interactive course, participants will learn the batch normalization and dropout for efficient training; optimizing MLP with transfer learning and implementing early stopping and model checkpoints; using TensorBoard for MLP training visualization; the parallel and distributed training for MLP and fine-tuning MLP models for optimal performance; deploying MLP models in production; the model explainability and interpretability and MLP in edge devices; the security and ethical considerations in AI; and the future of multilayer perceptrons in AI. 

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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