multi gpu deep learning

learning framework based on Theano. it is designed based on Torch and written in Python. it is a highly modular neural network library that supports GPU and CPU. 3. Lasagne (deep learning) It is not just a delicious Italian dish, but also a deep

multi-layered neural networks to learn more complex skills. 1987: Terry Sejnowski of Hopkins University has developed a nettalk system that is trained to pronounce, from random pronunciation to recognizable speech. 1990: In Bell Labs, LeCun uses a reverse propagation algorithm to train a network that can read handwritten words. AT/t later developed a machine that could read checks using this algorithm. 1995: The Bell Lab mathe

mainstream framework, of course, not to say that Keras and CNTK are not mainstream, the article does not have any interest related things, but the keras itself has a variety of frameworks as the back end, So there is no point in contrast to its back-end frame, Keras is undoubtedly the slowest. and CNTK because the author of Windows is not feeling so also not within the range of evaluation (CNTK is also a good framework, of course, also cross-platform, interested parties can go to trample on the

Deep Learning-nlplecture 2:introduction to TeanoEnter link description hereNeural Networks can be expressed as one long function of vector and matrix operations.(A neural network can be represented as a long function of a vector and a matrix operation.) )Common frameworks (Common frame) C + +If you are need maximum performance,start from scratch (and if you need the highest performance then start p

leader of Vapnik, support vector machine and nuclear method research. According to Scholkopf, Vapnik invented support vector machines to "kill" neural networks (He wanted to kill neural network). Support Vector machines are really effective, and a period of time support vector machines takes the upper hand.In recent years, the Master of Neural network Hinton has proposed the deep learning algorithm of Neur

Good memory is not as bad as writing, has always been only written to learn the habit of notes, has never written a blog. Now it is an honor to join the Zhejiang University Student AI Association, determined to follow the excellent teachers and seniors learn the AI field related technology, but also for the operation and Development of the association to contribute strength. Since September, because the scientific research needs to add a strong personal interest, has been insisting on

Mobileye and Nvidia use a convnet based approach in their upcoming automotive Vision systems. Other increasingly important applications relate to natural language understanding and speech recognition. Despite these achievements, Convnets was largely abandoned by the mainstream computer vision and machine learning community until the Imagenet race in 2012. When the deep convolution network was applied to da

This article source: http://suanfazu.com/t/caffe/281The main purpose of this article is to save a link and suggest reading the original.Caffe (convolutional Architecture for Fast Feature embedding) is a clear and efficient deep learning framework whose author is a PhD graduate from UC Berkeley and currently works for Google.Caffe is a pure C++/cuda architecture that supports command line, Python, and MATLAB

), variables (Variable). lesson three TensorFlow linear regression and simple use of classifications. The fourth lesson Softmax, cross-entropy (cross-entropy), dropout, and the introduction of various optimizations in TensorFlow. Fifth Lesson, CNN, and CNN to solve the problem of mnist classification. The sixth lesson uses Tensorboard to visualize the structure and visualize the process of the network operation. The seventh lesson is the explanation of recurrent neural network lstm and the use o

faster to compute large-scale data than sigmoid (imagine that if each layer Relu discards 50% of the useless data, then 4 of the data is the original 6%, Of course the real situation is not so simple), and relu another part of the derivative is 1, in the reverse propagation of the gradient is very convenient, sigmoid function is everywhere, but in the vast majority of the range of the derivative is very small, which makes it does not filter out the ability of the unwanted data, greatly reducing

, layer level, and data level at two levels [6]. For layer-level parallelism, many implementations use GPU arrays to compute layer-level activations in parallel and synchronize them frequently. However, this approach is not suitable for clusters where data resides on multiple machines connected over the network, because of the high network overhead. For data-tier parallelism, training is parallel to the data set and more suitable for distributed devic

no problem, understand the principle and code can modify parameters, make our own style. Tips:(1) Note that we also need to download the VGG model (placed under the current project), the runtime remember the path of the model to change to its current path (2) We can adjust the parameters, change the optimization algorithm, and even the network structure, try to see whether it will get better results, and we can do the style of video transformation OH (3) Neural style can not save the training m

A summaryIn this paper, we present a very simple image classification deep learning framework, which relies on several basic data processing methods: 1) Cascade principal component Analysis (PCA), 2) Two value hash coding, 3) chunking histogram. In the proposed framework, the multi-layer filter kernel is first studied by PCA method, and then sampled and encoded u

Original URL: http://www.iteye.com/news/312701. We should see deeper models, which can be learned from fewer training samples compared to today's models, and will make substantial progress in unsupervised learning. We should see more accurate and useful speech and visual recognition systems.2. I expect deep learning to be increasingly used for

7.27 after the summer vacation, I started to run the deep learning program after I completed the financial project. Hinton ran the article code on nature for three days, and then DEBUG changed the batch from 200 to 20. Later, I started reading articles and felt dizzy. It turns to: Deep Learning tutorials installs thean