【推荐】CNN最新进展综述(方法&应用)
本文全面的介绍、总结了最近几年卷积神经网络的发展，文章在introduction部分回顾了LeNet-5、AlexNet、ZFNet、VGGNet、GoogleNet和ResNet等网络结构；第二部分以LeNet-5为例重点介绍了卷积神经网络的基本组成部分；第三部分分别介绍了卷积层、池化层、激活函数、损失函数、正则化、优化的最新工作，例如卷积层中的NIN、Inception，池化层中的Lp池化、Stochastic pooling、Spectral pooling、Spatial pyramid pooling、Multi-scale Orderless Pooling等，激活函数中的ReLU、Leaky ReLU、Parametric ReLU、Randomized ReLU、ELU、Maxout等，损失函数中的Softmax loss、Hinge loss、Contrastive loss等；第四部分介绍了快速训练CNN的方法；第五部分从图像分类、物体检测、物体追踪、姿态估计、文本检测、行动识别、场景标记等角度介绍了CNN的应用；最后一部分进行总结。
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676204&idx=1&sn=e8f3af77440f40e054d9e50083494fe6#rd
【推荐】所有的Andrew Ng的机器学习课程练习和代码(Python)
所有的Andrew Ng的机器学习课程练习和代码，此外还包括Udacity上Google深度学习课程的练习、edX上Spark课程的练习等代码。
All of Andrew Ng's machine learning class in Python：
This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. The original code, exercise text, and data files for this post are available here.
Part 1 - Simple Linear Regression
Part 2 - Multivariate Linear Regression
Part 3 - Logistic Regression
Part 4 - Multivariate Logistic Regression
Part 5 - Neural Networks
Part 6 - Support Vector Machines
Part 7 - K-Means Clustering & PCA
Part 8 - Anomaly Detection & Recommendation
One of the pivotal moments in my professional development this year came when I discovered Coursera. I'd heard of the 'MOOC' phenomenon but had not had the time to dive in and take a class. Earlier this year I finally pulled the trigger and signed up for Andrew Ng's Machine Learning class. I completed the whole thing from start to finish, including all of the programming exercises. The experience opened my eyes to the power of this type of education platform, and I've been hooked ever since.
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676230&idx=1&sn=a322d220345a0227629abf1240e86f01#rd
【推荐】TensorFlow资源大全
TensorFlow资源大全包括书籍，教程，库，以及更多。
A curated list of 50+ awesome TensorFlow resources including tutorials, books, libraries, projects and more.
If you know of any awesome TensorFlow resources that you think should be added to this list, please let me know in the comments section.
And be sure to check out our other awesome lists of the best computer vision resources and free machine learning books.
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676207&idx=1&sn=6919e02ef4b65a13cd6e3ecda8ca1a12#rd
【推荐】Github上Stars最多的53个深度学习项目，TensorFlow遥遥领先
对于深度学习工作者而言，大量的开源项目避免了很多重造轮子的工作，降低了算法实现门槛。本文盘点了在Github上获得Stars最多的深度学习项目，供从业者参考。并感谢这些开源爱好者的贡献。
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676225&idx=1&sn=1fe19d0327832c921e5a1465dc8aaa8f#rd
【推荐】卷积神经网络的直观解释
这篇博文《An Intuitive Explanation of Convolutional Neural Networks》图文并茂将卷积神经网络的各个模块讲解的通俗易懂，同时总结了典型的卷积神经网络结构。
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676214&idx=1&sn=d87a9f62044c6308279e2df7238f4df6#rd
【学习】南京大学LAMDA所长周志华：机器学习的现状与未来
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676230&idx=3&sn=13f0b70901e9890ff3c4ed8056b47d8e#rd
【推荐】手把手教你用TensorFlow的深度学习进行图像修复
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676210&idx=1&sn=389580903985a1e57af9ce1e6df4afe8#rd
【学习】面试机器学习、大数据岗位时遇到的各种问题
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676210&idx=4&sn=be04ce221029f78f56612c1b91dfc188#rd
【学习】（论文+代码）斯坦福CVGL实验室ECCV2016新研究-在100帧每秒场景下利用深度回归网络进行目标的追踪
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676207&idx=2&sn=ae45e191ef2644142885457d12924259#rd
【学习】ML 工程师需了解的 10 大算法
链接：http://mp.weixin.qq.com/s?__biz=MzA4NDEyMzc2Mw==&mid=2649676207&idx=4&sn=f8376c1bfdf8a9252f63fce8fe6c6ccc#rd
