machine learning amd gpu

Brief History of the machine learningMy subjective ML timelineSince the initial standpoint of science, technology and AI, scientists following Blaise Pascal and Von Leibniz Ponder AbouT a machine which is intellectually capable as much as humans. Famous writers like JulesPascal ' s machine performing subtraction and summation–1642Machine

several days, and sometimes it's written in a circle, because can expand said place too much, write these content, I looked for some face questions to see, the theory part basically can cover, but involves the real application still need to take time to understand, the final parallel understanding is not thorough enough, Matrix multiplication I used the GPU to achieve, but did not touch a large number of data, and do not know where the real problem w

what is the factors that might influence the run-time performance of your C Odes.CUDA programming:a Developer ' s Guide to Parallel Computing with GPUsShane CookThis book provides an in-depth coverages of important aspects related to CUDA programming – a programming technique that C An unleash the unparalleled power of GPU computation. With CUDA and a affordable GPU card, you can run your data analysis pro

, you understand what is the factors that might influence the run-time performance of your C Odes.CUDA programming:a Developer ' s Guide to Parallel Computing with GPUsShane CookThis book provides an in-depth coverages of important aspects related to CUDA programming – a programming technique that C An unleash the unparalleled power of GPU computation. With CUDA and a affordable GPU card, you can run your d

The ZW edition · Halcon-delphi series of Original tutorialsYogurt Automatic classification script (machine learning, artificial intelligence)Halcon's powerful image processing ability often ignores its more robust machine learning, artificial intelligence.At least, the current domestic and overseas

gensim is called the "People's Theme Modeling tool", which focuses on Dirichlet division and variants, which support natural language processing, It is easier to combine NLP and other machine learning algorithms, and it also cites the Word2vec of Google's text notation based on recurrent neural networks. Five, OrangeOrange is a library with a graphical user interface that is quite complete in terms of cla

Natural Language Processing Scalanlp-set of machine learning and numerical computing LibrariesBreeze-numeric processing library for ScalaChalk-natural language processing database.Factorie-a deployable probabilistic modeling toolkit that uses the scala software library. It provides you with a concise language to create a graph of relational factors, evaluate parameters, and deduce them. Data analysis/Data

Please do not reprint without permission, original zhxfl,http://www.cnblogs.com/zhxfl/p/5287644.htmlDirectory:First, IntroductionSecond, the Environment configurationThird, run the demoIv. Hardware Configuration RecommendationsV. OtherFirst, IntroductionDeep learning multi-machine multi-card cluster has become the mainstream, relative to Caffe and mxnet two more active open source, purine appears more worth

Recently, the Facebook Artificial Intelligence Research Institute (FAIR) has open-source a series of software libraries to help developers build larger, faster, Gigaom reports. Model of depth learning. The Open Software Library is called a module on Facebook. Using them to replace the torch in the common development environment in the field of machine learning

an open source software library that uses a data flow graph (stream graphs) for numerical computations. A node (Nodes) represents a mathematical operation in a graph, and a line (edges) in a graph represents an array of multidimensional data, the tensor (tensor), that is interconnected between nodes. Its flexible architecture allows you to expand computing on a variety of platforms, such as one or more CPUs (or GPU), servers, mobile devices, and so o

represent the neural network layer, which is very efficient for linear algebra and similar to Numpy arrays. Decaf Decaf is a deep learning library recently released by UC Berkeley. In the challenges of Imagenet classification, it is found that its neural network implementation is very advanced (state of art ). Nolearn If you want to use the excellent Scikit-learn library API in deep learning, encapsulating

linear algebra and similar to numpy arrays.DecafDecaf is a recent deep learning library published by UC Berkeley, tested in the Imagenet Classification challenge, and its neural network implementation is very advanced (state of art).NolearnIf you want to use the excellent Scikit-learn Library API in deep learning, encapsulating the decaf Nolearn will make it easier for you to use it. It is the packaging fo

training time to reduce. For example, let's say we optimized our model training to reduce our training time to 2 hours. So in one weeks we can train the model 7*24/2=84 times, which means we can find the best λ in 84 λ. This gives you the most chance to meet the best λ. This is why we have to choose optimization is the fast convergence speed of the algorithm, why use GPU, multicore, clustering, and so on model training, why the industry with a strong

a complement. TensorFlow is flexible, mobile, easy-to-use, and fully open source. Based on distbelief speed, scalability, and the features that prepare the product, TensorFlow is doing even better. According to Google, in some benchmarks, TensorFlow's performance was twice times faster than that of distbelief.The extended support for TensorFlow built-in deep learning extends beyond this-any calculation that can be expressed using a computed flow grap