deep learning mlp

Deep learning articles and code collections for text categorizationOriginal: franklearningmachine Machine Learning blog 4 days ago [1] convolutional neural Networks for sentence classificationYoon KimNew York UniversityEMNLP 2014http://www.aclweb.org/anthology/D14-1181This article mainly uses CNN to classify sentences based on pre-trained word vectors. The auth

Why is very few schools involved in deep learning? Why is they still hooked on to Bayesian methods?First, this question assumes that every university should has a ' deep learning ' person. Deep learning are mostly used in vision (

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir undergraduate Wang YuxuanDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers How the inverse propagation algorithm wor

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 learning difficulties do no

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://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning. The content is actually ver

companies want to go to the company to brush the work card, but here we do not need it, using face recognition, see what I can do. When I come close, it will recognize my face and then say "Welcome" (Andrew NG), I can pass without a work-cards. Let's take a look at another situation, next to Lin Yuanqing, IDL (Baidu Deep Learning Laboratory) Director, he led the development of the face recognition system,

From self learning to deep network In the previous section, we used the self encoder to learn the characteristics of input to the Softmax or logistic regression classifier. These features are only learned using data that is not annotated. In this section, we describe how to fine-tune these features using the annotated data for further refinement. If you have a large number of tagged data, you can significan

Deep Learning Notes (i): Logistic classificationDeep learning Notes (ii): Simple neural network, back propagation algorithm and implementationDeep Learning Notes (iii): activating functions and loss functionsDeep Learning Notes: A summary of optimization methodsDeep

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 tutorial; Li Feifei's CNN tutorial; Caffe's offi

Understanding deep learning requires familiarity with some simple mathematical concepts: tensors (tensor), Tensor operations tensor manipulation, differentiation differentiation, gradient descent gradient descent, and more."Hello World"----MNIST handwritten digit recognition#coding: Utf8import kerasfrom keras.datasets import mnistfrom keras import modelsfrom keras import Layersfrom keras.utils i Mport to_ca

Deep learning and Growing pains"Editor 's note" Although deep learning has a great effect on the current development of AI, deep learning workers are not smooth sailing. Chris Edwards, published in the Communications of the ACM ar

Part III: Deep Learning vs SLAMSLAM group discussion is really fun. Before we go into the important "deep learning vs slam" "discussion, I should say that every seminar contributor agrees: Semantics are necessary to build a larger and better SLAM system. There are lots of interesting little conversations about the futu

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

(theta0_vals, theta1_vals, j_vals)%draw an image of the parameter and the loss function. Pay attention to using this surf to compare the egg ache, surf (x, y, z) is this,Wuyi%x,y is a vector, Z is a matrix, a mesh made of X, Y ( -*100 points) with each point of Z the% to form a graph, but how does it correspond, where the egg hurts is that the second element of your x and the first element of y are formed by the point Not and Z (2，1) value corresponds!! -% but and Z (1,2) corresponding!! Becau

C + + Primer Learning Notes _20_ class and Data Abstraction (6) _ Deep copy and shallow copy, empty class and empty array One, deep copy and shallow copyShallow copy: All variables of the copied object contain the same value as the original object, and all references to other objects still point to the original object. In other words, a shallow copy simply duplic

A Neural Network approach to context-sensitive Generation of conversational responsesLeverage Financial News to Predict the Stock price movements Using Word embeddings and deep neural NetworksMatchnet:unifying Feature and Metric learning for patch-based MatchingUnderstanding Neural Networks Through Deep visualizationCode:https://github.com/yosinski/

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use

In the previous article we brought out the network structure of Googlenet InceptionV1, in this article we will detail inception V2/V3/V4 's development process and their network structure and highlights.Googlenet Inception V2Googlenet Inception V2 in "Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift" appears, the largest The highlight is the batch normalization method, which plays the following role:

Deep Reinforcement Learning Guide: http://mp.weixin.qq.com/s?__biz=MzI1NTE4NTUwOQ==mid=2650324914idx=1sn= 0baaf404b3d8132243d08b55310de210scene=2srcid=062732p5u33rrnikuedslvxnfrom=timeline Isappinstalled=0#wechat_redirect Detailed in-depth study, build DQN Guide (based on neon framework):https://mp.weixin.qq.com/s?__biz=MzA3MzI4MjgzMw==mid=2650716425idx=1sn= bf52c653b7cd054ce721ce5be928c623 "Multiagent coop

Summary Person Re-identification (ReID) is a important task in computer vision. Recently, deep learning with a metric learning loss have become a common framework for ReID. In this paper, we propose a new metric learning loss with hard sample mining called margin smaple mining loss (MSML) which can achieve better accu