Deep Learning for Computer Vision

內容描述

Key FeaturesTrain efficient deep learning models to solve different problems in Computer Vision with the help of this comprehensive guidePerform object detection, image classification and more, by combining the power of Python, Keras and TensorflowContains practical examples using real-world datasets to apply the concepts of deep learning to various computer vision algorithms Book DescriptionThis book will not teach you what you already know - it directly jumps on to readying the environment required to train efficient deep learning models for a plethora of computer vision tasks such as object recognition, image classification and feature detection. In the process, you will leverage the power of Python, popular Deep Learning frameworks such as Keras and Tensorflow. You will implement the common architectures of deep learning such as convolutional neural networks, recurrent neural networks to work on your image data, with this book.By the end of the book, you will be confident to develop and train your own deep learning models and use them to solve your Computer Vision problems.What you will learnSetup up the environment for keras and tensorflowTrain a pet classification problem while training the first deep learning modelUse a pre-trained model for image retrieval problem by understanding the deeper layers of a modelLearn about the solutions available of object detection and train a pedestrian detection to understand the nuancesLearn about losses for similarity learning and a train a model for face recognitionTrain a model that can caption images by training image along with textAdvance the knowledge by learning Generative Adversarial Networks and train a model that can generate imagesExplore video classification problem and relate video to imagesLearn how to deploy the trained models across platforms