CNNs are revolutionizing several application domains, such as visual recognition systems, self-driving cars, medical discoveries, innovative e-commerce, and many more. This book gets you started with the building blocks of CNNs, while also guiding you through the best practices for implementing real-life CNN models and solutions. You will learn to create innovative solutions for image and video analytics to solve complex machine learning and computer vision problems.
This book starts with an overview of deep neural networks, with an example of image classification, and walks you through building your first CNN model. You will learn concepts such as transfer learning and autoencoders with CNN that will enable you to build very powerful models, even with limited supervised (labeled image) training data.
Later we build upon these learnings to achieve advanced vision-related algorithms and solutions for object detection, instance segmentation, generative (adversarial) networks, image captioning, attention mechanisms, and recurrent attention models for vision.
Besides giving you hands-on experience with the most intriguing vision models and architectures, this book explores cutting-edge and very recent researches in the areas of CNN and computer vision. This enable the user to foresee the future in this field and quick-start their innovation journey using advanced CNN solutions.
By the end of this book, you should be ready to implement advanced, effective, and efficient CNN models in your professional projects or personal initiatives while working on complex images and video datasets.
