deep learning gpu benchmarks

get started. David Silver has also recently published a short article on deep-enhanced learning. Deep Learning Framework : A lot of deep learning frameworks, the most famous three should be TensorFlow (Google), Torch (Facebo

Free and open source mobile deep The learning framework, deploying by Baidu. This is the simply deploying CNN on mobile devices with the low complexity and the high speed. It supports calculation on the IOS GPU, and is already adopted by the Baidu APP. size:340k+ (on ARM v7)Speed:40ms (for IOS Metal GPU mobilenet) or M

experiment with the CPU and # CPU_ONLY := 1 remove the previous # number. If you use the GPU and have cudnn acceleration , # USE_CUDNN := 1 Remove the previous # number. If you use Openblas, it will be BLAS := atlas changed and BLAS := open added BLAS_INCLUDE := /usr/include/openblas (the default matrix operations library in Caffe is Atlas, but Openblas has some performance optimizations, so it is recommended to change Openblas) Not to b

learning techniques. The work is lagging behind neural networks, but researchers have begun to work on effective training techniques, as well as expanding processing to operate on platforms like multi-core GPU machines."We have an additional algorithm burden, that is, to spread uncertainty around the network," Lawrence said. "This is the beginning of the algorithmic problem, but also here, we've had most o

The history of CNNIn a review of the 2006 Hinton their science Paper, it was mentioned that the 2006, although the concept of deep learning was proposed, but the academic community is still not satisfied. At that time, there was a story of Hinton students on the stage when the paper, machine learning under the Taiwan Daniel Disdain, questioned your things have a

Tf.variable_scope () and Tf.get_variables () interface. To ensure that each variable has a unique name and can easily modify the number of hidden nodes and the number of network layers, we recommend referencing the code in the project, especially when defining variables to bind Cpu,tensorflow using the GPU by default may cause parameter updates to be too slow. The code above is also common in production environments, whether it's training, implement

train a neural network in your navigator based on Universal JavaScript. Karpathy also wrote a Convnetjs introductory tutorial, as well as a concise browser demo project.Nine, MXNet. From Cxxnet, Minerva, purine and other projects of the developer's hand, mainly in C + + written. MXNet emphasizes the efficiency of memory usage and even the task of running image recognition on smartphones.Ten, Neon. Nervana Systems, a startup company, open source this May, and in some

one experiment in which a deep neural network is trained to look for characteristic visual features of biological cell Division, Cire?an says the training phase could has taken five months on a conventional CPU; "It took three days on a GPU." Yann LeCun, director of Artificial intelligence in Facebook and founding director of New York University ' s Center For Data Science, says, "before, neural networks w

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

/* 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

multiple languages, such as Python, R, and Julia. Mxnet also comes with a series of neural network guides and blueprints. It is also noteworthy that a related project uses JavaScript to implement mxnet in a browser environment where interested friends can test a graphics classification model. 6. Qix This is a library of GitHub versions of various computing and programming topics related to resources, including Node.js, Golang, and depth learning. The

Installation Environment: Win 10 Professional Edition 64-bit + Visual Studio Community.Record the process of installing configuration mxnet in a GPU-equipped environment. The process uses Mxnet release's pre-built package directly, without using CMake compilation itself. Online has a lot of their own compiled tutorials, the process is more cumbersome, the direct use of the release package for beginners more simple and convenient.The reason for choosin

TensorFlow and serving models of the product process. Serving Models in Production with TensorFlow serving: a systematic explanation of how to apply the TensorFlow serving model in a production environment. ML Toolkit: Introduces the use of TensorFlow machine learning libraries, such as linear regression, Kmeans and other algorithmic models. Sequence Models and the RNN API: Describes how to build high-performance sequence-to-sequence models and relat

] Microsoft cognitive TOOLKIT-CNTK [C + +] MXNet adapted by Amazon [C + +] Torch by Collobert, Kavukcuoglu Clement Farabet, widely used by Facebook [Lua] Convnetjs by Andrej Karpathy [JavaScript] Theano by Universitéde Montréal [Python] Deeplearning4j by startup Skymind [Java] Paddle by Baidu [C + +] Scalable Sparse Tensor Network Engine (Dsstne) by Amazon [C + +] Neon by Nervana Systems [Python Sass] Chainer [Python] H2O [Java] Brainstorm by Istituto dalle Molle di studi sull ' Intelligenza a

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

network in your navigator. Karpathy also wrote a Convnetjs introductory tutorial, as well as a concise browser demo project.Nine, MXNet. From Cxxnet, Minerva, purine and other projects of the developer's hand, mainly in C + + written. Mxnet emphasizes the efficiency of memory usage and even the task of running image recognition on smartphones.Ten, Neon. Nervana Systems, a startup company, open source this May, and in some benchmarks, neon, developed

Java" deep learning framework and the first commercial-level deep-learning open Source Library. Deeplearning4j, launched by Skymind in June 2014, uses DEEPLEARNING4J's many star companies such as Accenture, Chevrolet, and Bo's consulting and IBM.Deeplearning4j is a high-maturity,