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CODE: NAI09
DURATION: 3 Days | 5 Days | 10 Days
CERTIFICATIONS: CPD
This course provides a practical introduction to deep learning techniques used in image classification and object detection. It covers key concepts such as transfer learning, image annotation, and model evaluation using popular frameworks like TensorFlow and Keras. Participants will learn how to build, train, and deploy convolutional neural networks (CNNs) for visual recognition tasks. Through practical projects, participants will develop the skills needed to apply deep learning to real-world image and video analysis challenges.
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:
Learn the role of deep learning in solving image classification and object detection problems.
Build and train convolutional neural networks (CNNs) using TensorFlow or Keras.
Use transfer learning techniques to enhance model accuracy.
Prepare and annotate image datasets for model training.
Measure, evaluate, and fine-tune model performance.
Deploy deep learning models for real-world visual recognition tasks.
Incorporate trained models into end-to-end computer vision solutions.
Tackle real-life image recognition challenges across different sectors.
At the end of this course, you’ll understand:
This course is designed for AI engineers, data scientists, computer vision specialists, software developers, and technical professionals who want to apply deep learning to visual recognition problems. It is also suitable for researchers and students with a foundational understanding of Python and machine learning who wish to expand their knowledge in computer vision.
✓ Modern facilities
✓ Course materials and certificate
✓ Accredited international trainers
✓ Training materials and workbook
✓ Access to online resources
