Convolutional Neural Network Book

Convolutional Neural Networks CNN are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!

The book assumes that you are familiar with the traditional neural networks and with prior experience in python programming.Skills You will learnConvolution Neural NetworksCreating a dataset from scratchTensorflowKerasFastaiResnetsNeural Style Transfer.

book. Hands-On Convolutional Neural Networks with TensorFlow. by Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems book. Computer Vision Using Deep Learning Neural Network Architectures with Python and Keras

One of my favorite books on theoretical aspects of neural networks is Anthony and Bartlett's book quotNeural Network Learning Theoretical Foundationsquot. This book studies neural networks in the context of statistical learning theory. You will find loads of estimates of VC dimensions of sets of networks and all that fun stuff.

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

This book by Seth Weidman explains the inner working of neural networks. The book will teach the user how to apply convolutional neural networks, multilayer neural networks, and recurrent neural networks from scratch. The text is full of working code examples and mathematical explanations to understand neural networks better.

This book shows how to develop and optimize deep learning models with advanced architectures. It also demonstrates the subtleties of the algorithms at the core of convolutional neural networks. You will study advanced topics on CNN and object detection using Keras and TensorFlow.

Convolutional Neural Networks A Comprehensive Guide to the Foundations, Architectures, and Applications of CNNs in Deep Learning and AI. by Mason Leblanc. Book 1 of 1 Convolutional Neural Networks and Recurrent Neural Networks. 3.7 out of 5 stars. 5. Kindle. Price, product page 0.00 0. 00.

This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images

It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks training, regularization, and optimization of CNNs.The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some