8 Next-Gen Convolutional Neural Networks Books Defining 2025

What happens when deep learning expertise meets medical imaging challenges? Snehan Biswas, Amartya Mukherjee, and Nilanjan Dey crafted this guide to bridge the gap between advanced convolutional neural networks and practical medical application development. You’ll find detailed Python code examples and explanations on how to build neural networks that address real medical image analysis problems, including data augmentation techniques like GANs and stable diffusion. The book also explores emerging technologies such as GPT and Vision Transformers, making it a solid starting point if you’re a graduate student or researcher aiming to apply deep learning in healthcare contexts. Its focus on cloud-based solutions and object-oriented programming gives you hands-on skills beyond theory.

Unlike most books that treat video classification superficially, Khushpreet Kaur and Krishan Kumar focus on the nuanced power of 3D convolutional neural networks (3D CNNs) to capture both spatial and temporal features. They dive into training models on the UCF101 dataset and validate performance using a custom streaming-video dataset, offering concrete insights into real-time action recognition. You’ll find detailed explanations of the model framework and preprocessing steps, giving you a clear understanding of how 3D CNNs improve accuracy in surveillance and indexing systems. This slim volume is ideal if you want targeted knowledge on applying 3D CNNs specifically for video classification rather than general CNN theory.

This tailored book explores the latest advances and discoveries in convolutional neural networks as they emerge in 2025. It examines cutting-edge developments and evolving techniques, focusing on your unique interests and background to present the most relevant insights. The content matches your specific goals, ensuring a deep understanding of breakthrough CNN approaches shaping current AI research. By addressing recent innovations and trends, the book reveals how CNN architectures and applications are rapidly evolving, providing a rich learning experience tailored to your expertise and curiosity. This personalized guide helps you stay at the forefront of CNN technology by focusing precisely on what matters most to you.

Drawing from his experience in AI and computer vision, Martin Dunagan developed this guide to make convolutional neural networks accessible for practical use in visual tasks. You’ll learn foundational deep learning concepts, explore classic architectures like AlexNet and ResNet, and dive into advanced techniques such as transfer learning and image segmentation. Chapters dedicated to optimizing CNNs for deployment on everything from cloud servers to edge devices offer valuable insights. This book suits anyone eager to understand and implement CNNs, whether you’re a student, developer, or researcher aiming to keep pace with AI’s rapid evolution.

Drawing from his deep experience in AI development, Mason Leblanc crafted this guide to clarify the complex world of convolutional neural networks. You’ll gain a solid grasp of foundational concepts like convolution and pooling, then advance to mastering architectures such as ResNet and EfficientNet that are shaping current AI breakthroughs. The book’s narrative approach, featuring a mentor and learners tackling real challenges, helps you connect theory with practical application in fields like healthcare and autonomous vehicles. If you want to understand how CNNs power today’s AI innovations and build your own models, this book offers a clear and approachable path without oversimplifying the technical depth.

What happens when two AI entrepreneurs distill their hands-on experience into a guide for mastering convolutional neural networks? Henilsinh Raj and Nisharg Nargund crafted this book to bridge the gap between theory and application, walking you through CNN concepts alongside practical TensorFlow code. You’ll explore everything from convolution and pooling layers to transfer learning and image segmentation, supported by real examples and insights drawn from their own research and startups. This book suits anyone eager to deepen their understanding of CNNs, whether you're taking your first steps or looking to sharpen your skills with the latest techniques.

This tailored book explores emerging innovations in convolutional neural networks (CNNs) with a focus on your unique interests and professional goals. It examines the latest 2025 developments, breakthroughs, and research trends shaping CNN architectures and applications. By centering content around your background and objectives, the book reveals how upcoming CNN technologies might impact areas such as computer vision, video analytics, and hardware acceleration. This personalized approach ensures you engage deeply with cutting-edge knowledge relevant to your role, enabling you to anticipate and adapt to transformative shifts in AI. Readers gain an insightful, customized perspective that goes beyond general overviews to spotlight trends that matter most to them.

What started as a focused investigation into cocoa agriculture challenges became a detailed exploration of how Convolutional Neural Networks (CNNs) can revolutionize plantation monitoring. Miracle A. Atianashie walks you through leveraging deep learning on aerial imagery to identify diseases, pests, and nutrient issues affecting cocoa plants, giving you a toolkit for improving crop health with precision. You'll gain insights into integrating CNN technology with sustainable farming practices, learning how real-time data transforms decision-making for farmers facing climate and disease pressures. This book suits professionals interested in applying AI to agriculture, especially those eager to enhance crop monitoring with cutting-edge neural network models.

Drawing from his deep experience as an AI enthusiast and innovator, Falahgs Saleh offers a practical guide to harnessing Gemini Pro and GPT-4 for convolutional neural networks. You’ll gain hands-on knowledge through 100 detailed prompt recipes that showcase how to implement CNNs effectively with these advanced AI tools. The book breaks down complex interactions between Gemini Pro and GPT-4, helping you bridge theory and application, especially in areas like image recognition and pattern analysis. If you’re eager to apply cutting-edge AI tech beyond the basics, this book provides focused insights tailored for both newcomers and more experienced practitioners.

When the inefficiencies of traditional CNN deployment methods became apparent, Munir, Kong, and Qureshi drew on their expertise to examine accelerator technologies tailored for convolutional neural networks. You’ll gain a clear understanding of CNN architectures and the nuances of accelerator design, including lossless compression techniques for weights and feature maps, and hardware-software co-design strategies that improve performance in resource-limited environments. Chapters detail innovations like dense and sparse CNN accelerators, giving you insights into how edge computing and IoT devices can leverage these advances. If you’re involved in AI hardware or embedded systems, this book offers a focused dive into optimizing CNNs beyond typical software approaches.