Unlock the full potential of Convolutional Neural Networks for image recognition and analysis. In this course, Convolutional Neural Networks (CNNs): Visual Mastery with Deep Learning, you’ll gain the ability to build, train, and optimize CNN models for diverse and complex visual recognition tasks. First, you’ll explore the foundational elements of CNNs, delving into the intricacies of filters, pools, and convolutions. You’ll understand how these elements work together to extract and learn features from images. Next, you’ll discover how to architect and train effective CNN models. This includes designing custom CNN architectures for specific tasks, applying data augmentation for improved model generalization, and utilizing advanced techniques like transfer learning for enhanced performance. Finally, you’ll learn how to apply CNNs in real-world scenarios, understanding their applications in various industries and navigating the ethical considerations involved. You’ll evaluate case studies to appreciate the practical impact of CNNs and explore the latest trends shaping the future of computer vision. When you’re finished with this course, you’ll have the skills and knowledge of CNNs and deep learning needed to develop sophisticated visual recognition systems and apply them to real- world challenges.
Author Name: Axel Sirota
Author Description:
Axel Sirota is a Microsoft Certified Trainer with a deep interest in Deep Learning and Machine Learning Operations. He has a Masters degree in Mathematics and after researching in Probability, Statistics and Machine Learning optimisation, he works as an AI and Cloud Consultant as well as being an Author and Instructor at Pluralsight, Develop Intelligence, and O’Reilly Media.

Table of Contents

• Course Overview
  2mins
• Foundations of CNNs
  15mins
• Architecting and Training Effective CNN Models
  20mins