keras deep learning tutorial

Original address: https://www.zhihu.com/question/27982282 Gein Chen's answer many thanks —————————————————————————————————————————— 1. The first step of learning the program, first let the program run, see the results, so that there will be an intuitive feeling.Caffe's official Online Caffe | The Deep learning Framework provides a lot of examples, and you can eas

) In 2013, Nal Kalchbrenner and Phil Blunsom presented a new end-to-end encoder-decoder architecture for machine translation. In 2014, Sutskever developed a method called sequence-to-sequence (seq2seq) learning, and Google used this model to give a concrete implementation method in the tutorial of its deep learning fra

standard solution to solve the RBM. The RBM Solution section will be described in the next small article.OK, the first article is here.Resources[1] http://www.chawenti.com/articles/17243.html[2] Zhang Chunxia, restricted Boltzmann machine introduction[3] Http://www.cnblogs.com/tornadomeet/archive/2013/03/27/2984725.html[4] Http://deeplearning.net/tutorial/rbm.html[5] Asja Fischer, and Christian Igel,an Introduction to RBM[6] G.hinton, A Practical Gui

Preface: Recently, I intend to learn some theoretical knowledge of deep learing in a slightly systematic way, and intend to use Andrew Ng's Web tutorial Ufldl Tutorial, which is said to be easy to read and not too long. But before this, or review the basic knowledge of machine learning, see Web page: http://openclassro

JS doing deep learning, accidental discovery and introductionRecently I first dabbled with node. js, and used it to develop a graduation design Web module, and then through the call System command in node execution Python file way to achieve deep learning function module docking, Python code intervention, make JS code

Reprint http://blog.sina.com.cn/s/blog_4a1853330102v0mr.html Sparse Coding: This section will briefly describe the next sparse coding (sparse encoding), because sparse coding is also an important branch of deep learning, as well as extracting good features from datasets. The content of this article is refer to the Stanford Deep

time series signals. CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. CNNs as a deep learning architecture is proposed to minimize the preprocessin

Python to do deep learning caffe design CombatEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For

1. Study of "face recognition based on deep learning" in academic dissertation:The introduction of RBM and DBN is more detailed, it can be used as the basic reading and then read English paper.Derivation of 2.RBM:① Deep Learning notes-rbm_ Baidu LibraryThis is very straightforward, it feels very good! I don't know who

Deep Learning first battle: complete: ufldl tutorial sparse self-encoder-exercise: sparse autoencodercode: learned sparse parameter W1: References: Ufldl tutorial sparse self-Encoder Read autoencoders articles: [3] Hinton, G. E., osindero, S., teh, Y. (2006). A fast learning