kaggle 的 umich-sentiment-train.txt情感分析数据集

两种手写体数据集mnist.pkl.gz和t10k-labels-idx1-ubyte.gz，t10k-images-idx3-ubyte.gz，train-labels-idx1-ubyte.gz，train-images-idx3-ubyte.gz

数据集CoNLL-2003，这一数据集是用于测试命名实体识别的早期训练数据，文本来源是报纸新闻。英文数据eng.train

NLP情绪识别训练集，可直接用于模型训练，分为积极、消极和无情绪三种标签类型

HEV模型

提取于官网，费时较长，文档是原网页文件，为各位提供一点便利，不用在官网等待

RDO是红帽Red Hat Enterprise Linux OpenStack Platform的社区版，类似RHEL和Fedora，RHEV和oVirt这样的关系。

Learn how to model and train advanced neural networks to implement a variety of Computer Vision tasks Key FeaturesTrain different kinds of deep learning model from scratch to solve specific problems in Computer VisionCombine the power of Python, Keras, and TensorFlow to build deep learning models for object detection, image classification, similarity learning, image captioning, and moreIncludes tips on optimizing and improving the performance of your models under various constraintsBook Description Deep learning has shown its power in several application areas of Artificial Intelligence, especially in Computer Vision. Computer Vision is the science of understanding and manipulating images, and finds enormous applications in the areas of robotics, automation, and so on. This book will also show you, with practical examples, how to develop Computer Vision applications by leveraging the power of deep learning. In this book, you will learn different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more. You will also explore their applications using popular Python libraries such as TensorFlow and Keras. This book will help you master state-of-the-art, deep learning algorithms and their implementation. What you will learnSet up an environment for deep learning with Python, TensorFlow, and KerasDefine and train a model for image and video classificationUse features from a pre-trained Convolutional Neural Network model for image retrievalUnderstand and implement object detection using the real-world Pedestrian Detection scenarioLearn about various problems in image captioning and how to overcome them by training images and text togetherImplement similarity matching and train a model for face recognitionUnderstand the concept of generative models and use them for image generationDeploy your deep learning models and optimize them for high performanceWho This Book Is For This book is targeted

SRCNN源码分析：以文档的形式顺了一下训练时的代码流程和函数功能。只包含源码中的函数名及必要代码。可以帮助理解训练阶段的流程。

可从该页面获得的MNIST手写数字数据库具有60,000个示例的训练集和10,000个示例的测试集。它是NIST提供的更大集合的子集。数字已经过尺寸标准化，并以固定尺寸的图像为中心。 对于那些希望在实际数据上尝试学习技术和模式识别方法，同时在预处理和格式化方面花费最少的人来说，它是一个很好的数据库