Computer Vision Using Deep Learning

The book "Computer Vision Using Deep Learning" offers a comprehensive introduction to the application of deep learning techniques for solving computer vision problems. It covers key topics such as image classification, object detection, tracking, pose estimation, face recognition, and sentiment analysis. Readers are guided through various deep learning architectures and techniques that can be implemented using the Keras and TensorFlow libraries. The book provides a detailed analysis of several neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet. Each code example is thoroughly explained to ensure that the concepts can be successfully applied in their own projects. The combination of theory and practical applications makes this book a valuable resource for professionals in the fields of software development and data science.