COURSE OVERVIEW
IT0006 : 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 |
Request 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 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:
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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