/* author:cyh_24 *//* date:2014.10.2 *//* Email: [Email protected] *//* more:http://blog.csdn.net/cyh_24 */Recently, the focus of the study in the image of this piece of content, the recent game more, in order not to drag the hind legs too much, decided to study deeplearning, mainly in Theano the official course deep Learning tutorial for reference.This series of blog should be continuously updated, I hope
In the previous blog, we used the RBM-based deep autoencoder to compress the mnist data set, which should be said to have achieved good results. Here, we replace the neural network with the traditional fully-connected feedforward neural network to compress the Mnist data set to see what the similarities and differences between the two effects are. The entire code is still implemented using deeplearning4j, and we combine it with the spark platform to f
When does the deep learning model in NLP need a tree structure?Some time ago read Jiwei Li et al and others [1] in EMNLP2015 published the paper "When is the Tree structures necessary for the deep learning of representations?", This paper mainly compares the recursive neural network based on tree structure (Recursive n
Write in front:has not tidied up the habit, causes many things to be forgotten, misses. Take this opportunity to develop a habit.Make a collation of the existing things, record, to explore and share new things.So the main content of the blog for I have done, the study of the collation of records and new algorithms, network framework of learning. It's basically about deep
closer to the real neuron activation model. Bridging the gap with pre-training 2 about pre-training in deep learning 2.1 Why pre-training
Deep networking has the following drawbacks: The deeper the network, the more training samples are needed. If the use of supervision will require a large number of samples, or small-scale samples can easily lead to overfitting
, momentum=0.9, decay=0.0, Nesterov=false)
model.fit (train_set_x, train_set_y, validation_split=0.1, nb_epoch=200, batch_size=256, Callbacks=[lrate])
The above code is to make the learning Rate index drop, as shown in the following figure:
Of course, can also directly modify the parameters in the SGD declaration function to directly modify the learning rate, learning
CNN began in the 90 's lenet, the early 21st century silent 10 years, until 12 Alexnet began again the second spring, from the ZF net to Vgg,googlenet to ResNet and the recent densenet, the network is more and more deep, architecture more and more complex, The method of vanishing gradient disappears in reverse propagation is also becoming more and more ingenious.
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AlexNet
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Applied Deep Learning ResourcesA Collection of articles, blog posts, slides and code snippets about deep learning in applied settings. Including trained models and simple methods The can is used out of the box. Mainly focusing on convolutional neural Networks (CNN) But recurrent neural Networks (RNN),
Mark, let's study for a moment.Original address: http://www.csdn.net/article/2015-09-15/2825714Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is
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about the author
The author Dai is a deep learning enthusiast who focuses on the NLP direction. This article introduces the current status of machine translation, and the basic principles and processes involved, to beginners who are interested in deep
In fact, starting from this blog post, we are really into the field of deep learning. In the field of deep learning, the proven mature algorithm, currently has deep convolutional network (DNN) and recursive Network (RNN), in the field of image recognition, video recognition,
Setting up a deep learning machine from Scratch (software)A detailed guide-to-setting up your machine for deep learning. Includes instructions to the install drivers, tools and various deep learning frameworks. This is tested on a
Source: Michael Nielsen's "Neural Network and Deep leraning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Xu Wei (https://github.com/memeda)Statement: We will be in every Monday, Thursday, Sunday regularly serialized the Chinese translation of the book, if you need to reprint please contact [email protected], without authorization shall not be reproduced."This article is reproduced from" h
Today's two network structures are the latest in the industry for image processing problems proposed by the latest structure, the main solution is the Super deep network in training optimization problems encountered. To tell the truth, both models are not mathematically complex in themselves, but it does have a very good effect in combat (the deep residual network helps Microsoft's team to gain the 2015 Ima
One of the target detection (traditional algorithm and deep learning source learning)
This series of writing about target detection, including traditional algorithms and in-depth learning methods will involve, focus on the experiment and not focus on the theory, theory-related to see the paper, mainly rely on OPENCV.
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Caffe (convolution Architecture for Feature Extraction) as a very hot framework for deep learning CNN, for Beginners, Build Linux under the Caffe platform is a key step in learning deep learning, its process is more cumbersome, recalled the original toss of those days, then
Deep historyHistory of Deep learningThe roots of deep learning reach back further than LeCun ' s time at Bell Labs. He and a few others who pioneered the technique were actually resuscitating a long-dead idea in artificial intelligence.The root of deep
Recently in the NetEase Cloud classroom learning "depth study" micro-professional, the class after the programming work record down.
Deep Learning – Wunda
The logical regression written in Python before comparison
deeplearning operation Logistic regression with a neural network mindset
Welcome to your (required) programming assignment! You'll build a logistic reg
Original English: Teach Yourself Deep Learning with TensorFlow
Author: Vincent Vanhoucke,google chief Scientist. Translation: Guokai Han.
In recent years, deep learning has become one of the hottest topics in the field of machine learning. TensorFlow, as an open source proje
probability estimate. Merging the two best model in Figure 3 and Figure 4 to achieve a better value, the fusion of seven model will become worse.Ten. Reference[1]. Simonyan K, Zisserman A. Very deep convolutional Networks for large-scale Image recognition[j]. ARXIV Preprint arxiv:1409.1556, 2014.[2]. Krizhevsky, A., Sutskever, I., and Hinton, G. E. ImageNet classification with deep convolutional neural net
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