Deep Learning for Image Recognition

artificial intelligence, computer vision, neural networks, convolutional neural networks, feature extraction, image classification, object detection, image segmentation, dataset preparation, transfer

씨익북스 편집부 지음

타우루스 2025년 02월 12일 출간

작품소개

책소개

"Deep Learning for Image Recognition is a comprehensive guide designed for both beginners and experienced practitioners in the field of artificial intelligence and computer vision. This book delves into the intricacies of image recognition using deep learning techniques, particularly focusing on convolutional neural networks (CNNs), which have revolutionized the way machines understand visual data.

Readers will embark on a journey through the foundational concepts of neural networks and gradually progress to advanced topics such as feature extraction, image classification, object detection, and image segmentation. The book emphasizes the importance of dataset preparation, providing insights on how to curate, label, and preprocess data effectively to train robust models.

One of the standout features of this book is its coverage of transfer learning and data augmentation techniques, which are essential for enhancing model performance without the need for extensive datasets. Readers will learn how to leverage pre-trained models to save time and resources while improving accuracy.

Additionally, the book addresses various training techniques and the challenges of overfitting, ensuring that readers are equipped with the knowledge to fine-tune their models for optimal results. Performance evaluation methods are discussed in detail, allowing readers to assess their models rigorously.

Utilizing popular deep learning frameworks such as TensorFlow and Keras, along with practical applications of OpenCV, this book provides hands-on examples and projects that encourage experimentation and real-time processing capabilities. The inclusion of GPU acceleration techniques further aids in optimizing model training and inference speeds.

With a blend of theoretical knowledge and practical implementation, Deep Learning for Image Recognition serves as an essential resource for anyone looking to master the art of image recognition through deep learning. Whether you're aiming to build intelligent systems for autonomous vehicles, medical imaging, or security surveillance, this book will guide you through the complexities of developing state-of-the-art image recognition solutions."

목차

"1. Introduction to Deep Learning and Image Recognition
2. Overview of Artificial Intelligence in Vision
3. Understanding Computer Vision Fundamentals
4. The Role of Neural Networks in Image Processing
5. Convolutional Neural Networks Explained
6. Key Concepts in Feature Extraction
7. Techniques for Image Classification
8. Object Detection Methods and Applications
9. Introduction to Image Segmentation
10. Preparing Datasets for Deep Learning
11. The Importance of Transfer Learning
12. Enhancing Models with Data Augmentation
13. Fundamentals of Supervised Learning
14. Exploring Unsupervised Learning Techniques
15. Effective Training Techniques for Neural Networks
16. Evaluating Model Performance
17. Popular Deep Learning Frameworks
18. Getting Started with Python for Image Recognition
19. Building Models with TensorFlow
20. Using Keras for Rapid Prototyping
21. Image Processing with OpenCV
22. Leveraging GPU Acceleration for Training
23. Optimizing Deep Learning Models
24. Understanding Overfitting and Its Solutions
25. Real-Time Processing in Image Recognition
26. Applications of Deep Learning in Healthcare
27. Deep Learning in Autonomous Vehicles
28. Utilizing Image Recognition in Retail
29. Security and Surveillance Applications
30. Enhancing User Experience with Image Recognition
31. The Future of Deep Learning in Vision
32. Challenges in Image Recognition
33. Ethical Considerations in AI and Image Processing
34. Case Studies of Successful Implementations
35. Understanding the Model Training Process
36. Hyperparameter Tuning for Optimal Performance
37. Visualizing Neural Network Outputs
38. The Role of Activation Functions
39. Transfer Learning for Limited Data Scenarios
40. Leveraging Pre-trained Models
41. The Impact of Batch Normalization
42. Implementing Dropout to Prevent Overfitting
43. The Importance of Cross-Validation
44. Advanced Techniques in Object Detection
45. Image Recognition in Natural Language Processing
46. Exploring Generative Adversarial Networks
47. The Role of Convolutional Layers
48. How to Build an Image Recognition Pipeline
49. Best Practices for Model Deployment
50. Future Trends in Deep Learning for Image Recognition"

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