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CODE: NAI03
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course teaches the core concepts and hands-on skills to build, train, and deploy deep neural networks. Participants will work with CNNs, RNNs, and Transformers using TensorFlow and PyTorch. Topics include backpropagation, optimization, regularization, and hyperparameter tuning. Participants will apply deep learning to images, text, and sequence data. The course prepares participants to solve real-world AI problems and build scalable models.
This course is available in the following formats:
Virtual
Classroom
Request this course in a different delivery format.
Course Outcomes
Delegates will gain the knowledge and skills to:
Understand the foundations of deep neural networks.
Implement and train CNNs, RNNs, and Transformers for various tasks.
Apply best practices in regularization, optimization, and tuning.
Build models for computer vision, natural language processing, and time-series forecasting.
Evaluate and improve model performance using advanced techniques.
Work with TensorFlow and PyTorch to develop deep learning projects.
Deploy deep learning models in real-world applications.
Analyze and interpret model behavior and outputs.
At the end of this course, you’ll understand:
This course is designed for data scientists, AI engineers, machine learning practitioners, software developers, and technical professionals who want to specialize in deep learning. It is important for researchers and graduate students seeking practical and theoretical expertise in neural networks. A solid understanding of Python and basic machine learning concepts is recommended.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
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