The Best Convolutional Neural Network Books for Beginners
Start your journey with the best convolutional neural network books for beginners, recommended by leaders, experts, and readers worldwide
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1
| 2017 | 168 Pages
This book covers the fundamentals in designing and deploying techniques using deep architectures. It is intended to serve as a beginner's guide to engineers or students who want to have a quick start on learning and/or building deep learning systems. This book provides a good theoretical and practical understanding and a complete toolkit of basic information and knowledge required to understand and build convolutional neural networks (CNN) from scratch. The book focuses explicitly on convolutional neural networks, filtering out other material that co-occur in many deep learning books on CNN topics.
Best for Engineers and students in technology.
- New York Times Bestseller
2
| 2024 | 211 Pages
Welcome to 'Harnessing Gemini Pro & GPT-4: 100 Practical Prompts for Convolutional Neural Networks.' In this groundbreaking book, we dive into the powerful synergy between Gemini Pro and GPT-4, unlocking a new realm of possibilities in implementing convolutional neural networks (CNNs). Whether you're a seasoned AI enthusiast or a curious beginner, these meticulously crafted prompts provide a hands-on approach to leveraging cutting-edge technology for real-world applications. Get ready to unleash the full potential of Gemini Pro and GPT-4 as we embark on a journey of innovation and discovery in the realm of CNNs.
Best for AI enthusiasts and technology professionals.
⭐
| 2025 | 30-300 pages
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3
| 2024 | 184 Pages
This book serves as a source of introductory material and reference for medical application development and related technologies by providing the detailed implementation of cutting-edge deep learning methodologies. It targets cloud-based advanced medical application developments using open-source Python-based deep learning libraries. It includes code snippets and sophisticated convolutional neural networks to tackle real-world problems in medical image analysis and beyond.
Features:.
Provides programming guidance for creation of sophisticated and reliable neural networks for image processing.
...moreIncorporates the comparative study on GAN, stable diffusion, and its application on medical image data augmentation.
Focuses on solving real-world medical imaging problems.
Discusses advanced concepts of deep learning along with the latest technology such as GPT, stable diffusion, and ViT.
Develops applicable knowledge of deep learning using Python programming, followed by code snippets and OOP concepts.This book is aimed at graduate students and researchers in medical data analytics, medical image analysis, signal processing, and deep learning.
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