7 Convolutional Neural Network Books That Define the Field

This book emerges from the combined expertise of four leading scientists deeply embedded in medical imaging and computer science, each bringing unique perspectives from institutions like the NIH and Siemens Healthcare. It thoroughly examines how convolutional neural networks reshape medical image computing, especially through semantic object detection and segmentation in both 2D and 3D contexts. You’ll find detailed explorations of applying deep learning to large-scale radiology databases, including an innovative method for integrating text and image mining. If you're involved in precision medicine or medical AI research, this book offers concrete frameworks and case studies that refine your understanding of high-performance image analysis.

Drawing from his role as a teaching assistant at Menoufia University and his MSc in Computer Science, Ahmed Fawzy Gad crafts a detailed guide to deploying deep learning for computer vision using CNNs and Python. You’ll move beyond theory, building neural networks from scratch, including artificial neural networks optimized with genetic algorithms, before mastering convolutional neural networks for image recognition. The book walks you through practical implementations using TensorFlow and Kivy, culminating in deploying models on web servers and cross-platform apps. It suits data scientists and engineers ready to translate CNN concepts into working applications, though those seeking purely theoretical explorations might find its hands-on approach more valuable.

This tailored book on convolutional neural networks (CNNs) offers a rich exploration of core concepts and advanced techniques, designed specifically to align with your unique background and goals. It examines the architecture, training processes, and practical applications of CNNs, providing a pathway that matches your interests and current experience level. Through a personalized lens, it reveals how different layers function and how models learn to identify complex patterns, bridging theoretical understanding with hands-on insights. This focused approach ensures you engage deeply with the content most relevant to your ambitions, making complex ideas accessible and meaningful.

The breakthrough moment came when Iffat Zafar, an AI engineer with a Ph.D. in Computer Vision and Machine Learning, translated years of academic research and industry experience into this hands-on guide. You learn to harness TensorFlow and Python to train convolutional neural networks tackling core computer vision tasks like image classification, object detection, and semantic segmentation. The book walks you through setting up a TensorFlow environment, building scalable models, and applying transfer learning and generative models such as autoencoders and GANs. It's particularly suited for software engineers and data scientists ready to deepen their practical skills in CNNs within real-world applications.

Umberto Michelucci draws on extensive research experience and practical expertise in data science and machine learning to explore the complexities of convolutional neural networks (CNNs) and object detection. The book guides you through foundational concepts like convolution and pooling before advancing to sophisticated architectures such as inception networks and resnets, enriched with hands-on guidance for using Keras and TensorFlow. You’ll gain insights into customizing model training with callbacks, leveraging eager execution, and implementing the YOLO algorithm for object detection. This resource suits scientists and researchers familiar with Python and machine learning who want to deepen their technical skills and practical application of CNNs.

When Perez, an expert in deep learning and neural networks, developed this book, his goal was to bridge the gap between complex theory and practical application using MATLAB's Neural Network Toolbox. You learn how to build, train, and deploy convolutional neural networks for image recognition, including techniques like transfer learning and feature extraction from pretrained models. The book walks you through MATLAB functions, classes, and workflows for classification, clustering, and autoencoders, empowering you to harness parallel computing for large datasets. If you're seeking hands-on skills to implement CNNs with MATLAB in research or industry settings, this book provides focused guidance without unnecessary jargon.

This tailored book meticulously explores the core principles and advanced techniques of convolutional neural networks, designed specifically to match your background and learning goals. It offers a personalized pathway that focuses on your interests, providing daily exercises and clear explanations to deepen your understanding of CNN architectures, training processes, and application scenarios. By synthesizing extensive knowledge into a focused 30-day plan, it accelerates your journey from foundational concepts to practical proficiency. The tailored content ensures you engage with material relevant to your skill level, helping you grasp complex topics like convolutional layers, feature extraction, and model optimization with confidence and clarity.

After analyzing key developments in computer vision, Ranjith M S developed this concise guide focusing on convolutional neural networks (CNNs). You’ll find practical instruction on building datasets, using TensorFlow and Keras frameworks, and applying advanced techniques like ResNets and neural style transfer. The book assumes you already know traditional neural networks and Python, making it suitable for practitioners looking to deepen their skills specifically in CNN architectures and applications. Chapter examples emphasize hands-on coding and understanding CNN components rather than theoretical abstractions.

After years immersed in computer vision research and hands-on work with autonomous vehicle technologies, Ragav Venkatesan developed this guide to demystify convolutional neural networks (CNNs) for engineers and students alike. You’ll gain a focused, practical understanding of CNN fundamentals, learning to build these models from the ground up without distraction from unrelated deep learning topics. The book’s clear explanations and concentrated toolkit cover essential concepts such as convolutional layers and filtering techniques, making it well suited for those seeking a direct path into CNN design and deployment. If you want a no-frills, technically sound introduction to CNNs specifically for visual computing, this book fits the bill.