deep learning with tensorflow course

learning research results in the era of output, deep learning papers published and miscellaneous, if there are errors please contact me, of course, if there is a better paper recommendation, please also inform, greatly appreciated. At the beginning of everything, this blog's original paper, mainly from other people's

two ways to configure deep learning environments: 1. Install directly on the development machine (note that other programs that rely on Python to run may not work): sudo pip installtensorflow-1.2.0rc2-cp27-cp27mu-manylinux1_x86_64.whl-ihttp://mirrors.aliyun.com/pypi/simple/-- Trusted-host mirrors.aliyun.com The cp27 of the WHL file means that using python2.7 cp27m is the ABI attribute 2. Virtualization in

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature

Deep reinforcement learning with Double q-learningGoogle DeepMind　　AbstractThe mainstream q-learning algorithm is too high to estimate the action value under certain conditions. In fact, it was not known whether such overestimation was common, detrimental to performance, and whether it could be organized from the main body. This article answers the above question

1. Preface In the process of learning deep learning, the main reference is four documents: the University of Taiwan's machine learning skills open course; Andrew ng's deep learning tut

Voice Command Data set address: http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz Audio Recognition Tutorial Address: https://www.tensorflow.org/versions/master/tutorials/audio_recognition At Google, we are often asked how to use deep learning to solve speech recognition and other audio recognition problems, such as detecting keywords or commands. Although there are already many large open-s

The theme report of "Transfer model of deep learning" shorthand and commentary (iv) Bai Chu of the Red bean Family concern 2017.11.04 22:33* 3275 reading 141 comments 0 like 0 The author presses: machine learning is moving towards a new era of interpretive models based on "semantics". Migration learning is likely to ta

The Wunda "Deep learning engineer" Special course includes the following five courses: 1, neural network and deep learning;2, improve the deep neural network: Super parameter debugging, regularization and optimization;3. Structure

columnsHttps://zhuanlan.zhihu.com/p/20878530?refer=intelligentunit Stanford cs231n Course Assignment # 1 Introduction-Smart Unit-Know the columnhttps://zhuanlan.zhihu.com/p/21441838 Stanford cs231n Course Assignment # 2 Introduction-Smart Unit-Know the columnhttps://zhuanlan.zhihu.com/p/21941485 Stanford cs231n Course Assignment # 3 Introduction-Smart Unit-Know

these features are added to the original features, the accuracy can be greatly improved, and the classification problem is even better than the current best classification algorithm! This method is called autoencoder. Of course, we can add some constraints to get a new deep learning method, for example, if the regularity limit of L1 is added on the basis of auto

This article is from: Http://jmozah.github.io/links/Following is a growing list of some of the materials I found on the web for deep Learni ng Beginners. Free Online Books Deep learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and deep learn