LazyProgrammer, "Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow" 2016 | ASIN: B01FQDREOK | 52 pages | EPUB | 1 MB This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This book is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST. In this course we are going to up the ante and look at the StreetView House Number (SVHN) dataset - which uses larger color images at various angles - so things are going to get tougher both computationally and in terms of the difficulty of the classification task. But we will show that convolutional neural networks, or CNNs, are capable of handling the challenge! Because convolution is such a central part of this type of neural network, we are going to go in-depth on this topic. It has more applications than you might imagine, such as modeling artificial organs like the pancreas and the heart. I'm going to show you how to build convolutional filters that can be applied to audio, like the echo effect, and I'm going to show you how to build filters for image effects, like the Gaussian blur and edge detection. After describing the architecture of a convolutional neural network, we will jump straight into code, and I will show you how to extend the deep neural networks we built last time with just a few new functions to turn them into CNNs. We will then test their performance and show how convolutional neural networks written in both Theano and TensorFlow can outperform the accuracy of a plain neural network on the StreetView House Number dataset.

Distributed Computing with Python by Francesco Pierfederici AZW3/MOBI/EPUB/PDF 多版本 This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

玛雅人的Red Nine Studio Pack 联系人：Mark Jackson技术动画总监电子邮件： 首先，感谢您的关注，如果您想得到更多的参与，请给我发邮件！ 这个Maya Python模块是一个正在进行的项目，目的是为那些工作室提供广泛的支持，而无需他们自己的研发部门。 令人遗憾的是，开箱即用的Maya缺少大型工作室通过其自己的工具集表现出的那种爱，这种StudioPack旨在纠正这种平衡。 有关建议和信息，请随时与我联系。 在installer文件夹中安装说明，还有完整的api文档;）

在本文中，我们开始创建自定义对象检测模型的过程。

涵盖opencv自带的"csrt"，"kcf"，"boosting"，"mil"，"tld"，"medianflow"，"mosse"，完整示例

入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 入门级别的图书管理系统，有数据库文件，有简单界面，可以实现图书信息的增加、修改、删除和查看，很简单，有两个主要界面 mysql wxpyth 图书管理系统 Gui ----------------------------------------- 2019 2022 三年进阶之路

垃圾短信识别 源码+模型+数据集全套，个人毕设项目，可直接运行

Python实现基于人脸识别的上课考勤系统。 这个人脸识别考勤签到系统是基于大佬的人脸识别陌生人报警系统二次开发的。 项目使用Python实现，基于OpenCV框架进行人脸识别和摄像头硬件调用，同时也用OpenCV工具包处理图片。交互界面使用pyqt5实现。 该系统实现了从学生信息输入、人脸数据录入、人脸数据训练，学生信息多条件搜索、修改，多选删除，人脸数据训练，人脸识别、追踪、签到等完整流程的各项功能。甚至允许生成签到表格和导出Excel格式签到表。 根据功能分配，系统分为三个部分实现各部分流程， 1. 录入端负责数据导入， 2. 管理端负责数据删改查以及人脸数据训练， 3. 监控端负责人脸识别以及签到功能。

用yolov5算法实现cow（牛）体检测识别，模型已经训练完毕，存放路径在runs/train目录下，模型可以直接拿来使用，检测效果见runs/detect目录下