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COURSE OVERVIEW

IT0006 : Neural Networks & Deep Learning
Neural Networks & Deep Learning
OVERVIEW
COURSE TITLE : IT0006 : Neural Networks & Deep Learning
COURSE DATE : Apr 06 - Apr 10 2025
DURATION : 5 Days
INSTRUCTOR : Dr. George Chel
VENUE : Dubai, UAE
COURSE FEE : $ 5500
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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 Neural Networks and Deep Learning. It covers the AI, machine learning and deep learning; the basics of artificial neural networks (ANN); the fundamentals of deep learning, neural network architecture, cost function and optimization in neural networks; setting up deep learning environment; the backpropagation and gradient descent including hyperparameter tuning and regularization; the activation functions and their role and evaluating neural networks; and the data preprocessing for neural networks. 
 
Further, the course will also discuss the implementation of first neural network with TensorFlow/PyTorch and convolutional neural networks (CNNs); the convolutional layers, pooling and fully connected layers; implementing CNNs with TensorFlow and PyTorch; how transfer learning speeds up training; using pre-trained models from TensorFlow/Keras; and the image data augmentation techniques. 

During this interactive course, participants will learn the overfitting in CNNs and how to handle it and computational efficiency in deep networks; the batch normalization for stabilizing training and hardware acceleration (GPUs & TPUs); the recurrent neural networks (RNNs) and time-series data, long short-term memory (LSTM), gated recurrent units (GRU), word embeddings and Word2Vec; the natural language processing (NLP) with deep learning, attention mechanism and transformers; the natural language processing (NLP) applications; the generative adversarial networks (GANs); the autoencoders for anomaly detection, reinforcement learning and deep Q-networks (DQN); the distributed training with TensorFlow and model deployment using TensorFlow serving; the cloud-based deep learning and optimization techniques for large-scale networks; the ethical considerations in AI and deep learning; and building and deploying a deep learning model. 

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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