Preface Deep learning is a fascinating field. Artificial neural networks have been around for a long time, but something special has happened in recent years. The mixture of new faster hardware, new techniques and highly optimized open source libraries allow very large networks to be created with frightening ease. This new wave of much larger and much deeper neural networks are also impressively skillful on a range of problems. I have watched over recent years as they tackle and handily become state-of-the-art across a range of difficult problem domains. Not least object recognition, speech recognition, sentiment classification, translation and more. When a technique comes a long that does so well on such a broad set of problems, you have to pay attention. The problem is where do you start with deep learning? I created this book because I thought that there was no gentle way for Python machine learning practitioners to quickly get started developing deep learning models. In developing the lessons in this book, I chose the best of breed Python deep learning library called Keras that abstracted away all of the complexity, ruthlessly leaving you an API containing only what you need to know to efficiently develop and evaluate neural network models. This is the guide that I wish I had when I started apply deep learning to machine learning problems. I hope that you find it useful on your own projects and have as much fun applying deep learning as I did in creating this book for you.

resnet+attention model. 利用tensorflow改写。绝对好用！

视频监控系统在交通、边防、公共安全等领域得到普遍的应用，智能化、网络化、高清化是其必然的发展趋势。通过在监控系统中集成智能视频分析算法，可以变被动监控为主动监控，变事后分析为事中分析，甚至事前预警，以达到及时处理应对紧急状况的目的。本论文基于开源计算机视觉库OpenCV开发了一款嵌入式智能监控系统。 本系统的硬件平台基于ARM CortexTM-A8处理器S5PV210，同时具备远程视频监控功能和人脸检测智慧功能。系统人机操作界面使用Qt图形库开发；系统视频采集模块基于V4L2驱动框架设计，并实现监控视频的本地显示；远程监控部分通过S5PV210芯片内置的MFC视频硬编码器实现对监控视频的H.264硬压缩，然后依照RTP/RTCP协议将压缩后的H.264码流发送到Internet，使用VLC软件验证监控视频网络传输的有效性。人脸检测部分使用Adaboost算法，基于开源计算机视觉库OpenCV开发。 经过测试，系统达到预期的设计目标。本文的重要意义还在于搭建了一种基于OpenCV的嵌入式智能监控系统开发平台，基于该平台可以根据不同的需求开发出更多的智能监控系统。

Deep Learning with PyTorch,介绍了PyTorch在深度学习上的应用。

deep learning with pytorch英问书籍，入门pytorch必备书籍。。

The Elements of Statistical Learning 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Deep Learning with Python A Hands-on Introduction Authors: Ketkar, Nihkil Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.

CMU最新深度学习课件 GAN模型 Benjamin Striner Generative adversarial networks (GANs) are relatively new. They have spawned a flurry of activity and progress in recent years. Goodfellow et al.

作者：Microsoft Research AI首席科学家 - 邓力 俞栋 This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

This book is for engineers, data analysts, and data scientists, interested in deep learning, and those looking to explore and implement advanced algorithms with PyTorch. Knowledge of machine learning is helpful but not mandatory. Knowledge of Python programming is expected.