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Title: Convolutional book Neural Networks for Convolutional network computer vision for computer vision (new architecture, Learning Transfer, Fine Tuning, and Pruning) Author: Vegetable Rahman Page: x, 132 pgs, Uk: 17.5x25 cm Isbn: 978-623-02-3539-9 Mold: October 2021 Synopsis: Computer vision is a field of science that learns and interprets objects or information on digital imagery. A smart camera with computer vision can monitor visual-based objects. Neural Network (CNN) convolutional is one of the quite popular methods and the most interested in vision problems due to its high level of accuracy. Cnn has expanded rapidly marked by numerous appearance of CNN architecture for diverse cases of object classification on digital imagery. This architecture is referred to as Existing CNN. besides creating a new CNN architecture. The book exposes how to use Existing CNN such as transfer learning, fine-tuning, and pruning. Learning transfer is the first approach to be applied when encountered with new image introduction problems. The way the transfer learning works is to utilize the entire weight or knowledge of the CNN network. The Existing Weight of CNN when trained to classify large data is maintained and used to classify data and fewer classes. Transfer learning into the fastest solution in dealing with classification problems without having to retrain millions of images. To be able to use learning transfers. Fine-tuning is a way to avail CNN Existing architecture by retraining dataset. Fine-tuning not only changes the Fully Connected part only but a layer of convolution and pooling can be changed. A convolution layer part such as activation function, padding, kernel and pooling layer can be changed to adjust needs. Fine-tuning requires greater computation compared to learning transfers. Further is a pruning, pruning or trimming Existing CNN is used to get a compact and faster CNN model in object classification. Pruning is the last choice in exploiting Existing CNN. Observation against learning transfer or fine-tuning of CNN architecture can be availed to choose the best architecturals. Thus, after reading this book is expected to create a new CNN architecture or can exploit Existing CNN.