bengio deep learning

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

As a deep learning platform developed by Baidu, paddjavasaddle is easy to learn, easy to use, and flexible and efficient, greatly reducing developers' R D thresholds. To help developers build a fast and Advanced path to deep learning, Baidu opened the "Deep

algorithm called Rmsprop can also be used to accelerate the mini-batch gradient decline, it is on the basis of MOMENTUAM modified, the formula as shown, DW into the square of the DW, in the fall when more divided by a radical. Can be understood as the vertical direction of the differential term is relatively large, so divided by a larger number, the horizontal direction of the differential term is relatively small, so divided by a relatively small number, so that can eliminate the downward swin

In the previous sections, we have covered what is target detection and how to detect targets, as well as the concepts of sliding windows, bounding box, and IOU, non-maxima suppression.Here will summarize the current target detection research results, and several classical target detection algorithms to summarize, this article is based on deep learning target detection, in the following sections, will be spe

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

Click on the "ZTE developer community" above to follow us Read a first-line developer, a good article every day 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

Deep Learning Source code Collection-Continuous update ... Zouxy09@qq.com Http://blog.csdn.net/zouxy09 Collected some source code for deep learning. The main is MATLAB and C + +, of course, there are python. Put it here and follow up with new updates that will continue. The table below is also welcome to be available

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

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.　　　　 LeNet AlexNet Zf Vgg Googlenet ResNet Densenet

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

ObjectiveFor deep learning, novice I recommend to see UFLDL first, do not do assignment words, one or two nights can be read. After all, convolution, pooling what is not a particularly mysterious thing. The course is concise, sharply, and points out the most basic and important points.cs231n This is a complete course, the content is a bit more, although the course is computer vision, but 80% is the content

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

Directory1. What is regularization?2. How does regularization reduce overfitting?3. Various regularization techniques in deep learning:Regularization of L2 and L1DropoutData Enhancement (augmentation)Stop early (Early stopping)4. Case study: Case studies using Keras on Mnist datasets1. What is regularization?Before going into this topic, take a look at these pictures:Have you seen this picture before? From left to right, our model learns too much deta

　　Prior to the China Software Cup competition, we used the relevant algorithms of deep learning in the contest, and also trained some simple models. The project on-line platform is a Web application written in Java, and deep learning uses the Python language, which involves the method of invoking the Python language in

implementation, this time we will not be affected by the list of copies, regardless of whether we operate directly on the list or on other data structures nested inside the list. Let's look at the state of these variables in memory:Looking at the above, we know the principle of deep copy. In fact, deep copy is to re-open a piece of space in memory, no matter how complex the data structure, as long as it en