Deep Learning In Action Image And Video Processing For Practical Use
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Deep Learning in Action: Image and Video Processing for Practical Use
Artificial intelligence technology has entered an extraordinary phase of fast development and wide application. The techniques developed in traditional AI research areas, such as computer vision and object recognition, have found many innovative applications in an array of real-world settings. The general methodological contributions from AI, such as a variety of recently developed deep learning algorithms, have also been applied to a wide spectrum of fields such as surveillance applications, real-time processing, IoT devices, and health care systems. The state-of-the-art and deep learning models have wider applicability and are highly efficient. Deep Learning in Action: Image and Video Processing for Practical Use provides a comprehensive and accessible resource for both intermediate to advanced readers seeking to harness the power of deep learning in the domains of video and image processing. The book bridges the gap between theoretical concepts and practical implementation by emphasizing lightweight approaches, enabling readers to efficiently apply deep learning techniques to real-world scenarios. It focuses on resource-efficient methods, making it particularly relevant in contexts where computational constraints are a concern. - Provides step-by-step guidance on implementing deep learning techniques, specifically for video and image processing tasks in real-world scenarios - Emphasizes lightweight and efficient approaches to deep learning, ensuring that readers learn techniques that are suited to resource-constrained environments - Covers a wide range of real-world applications, such as object detection, image segmentation, video classification - Offers a comprehensive understanding of how deep learning can be leveraged across various domains - Encourages hands-on experience that can be applied to the concepts to existing projects
Practical Deep Learning for Cloud, Mobile, and Edge
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Advanced Technology for Human Movement Rehabilitation and Enhancement
In the dynamic landscape of human movement science, the convergence of cutting-edge technology with the intricacies of human physiology has ushered in a new era of transformative possibilities. This captivating research theme, encompassing the realms of neurorehabilitation, assistive robotics, human-machine interaction, and balance control, offers the exciting prospect of reshaping the boundaries of movement recovery. Translational research serves as the crucial conduit between scientific breakthroughs and tangible implementation, ensuring that the cutting-edge technologies birthed within laboratories seamlessly transition into real-world clinical applications. Neurorehabilitation delves into neuroplasticity, unraveling the brain's adaptability for recovery. Advanced imaging and neurophysiology guide interventions, enabling rewiring of neural pathways in individuals with neurological injuries. Assistive robotics fuse human potential with sophisticated devices, aiding recovery through tailored support. These allies enhance muscle activation, joint movement, and gait training, driving functional independence. Human-machine interaction deciphers neural signals, blurring lines between humans and technology. A seamless collaboration empowers individuals to guide and benefit from technological assistance. Investigating balance control strategies is crucial for designing assistive robotics that revolutionize rehabilitation and mobility. By understanding users' perspectives, we uncover the intricate link between human thinking, neuromuscular coordination, and balance maintenance. This insight informs the development of user-centered robotics, enhancing natural movement patterns and enabling effective rehabilitation, ultimately restoring stability and confidence in mobility.