gpu workstation deep learning

), 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

, 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

fast.CNTK Simple and fast.TensorFlow uses only CUDNN v2, but even so its performance is still 1.5 times times slower than V2 with CUDNN torch, and there is a memory overflow problem in training googlenet when the batch size is 128.Theano performance on large networks is comparable to TORCH7. But its main problem is that the boot time is particularly long because it needs to compile the C/cuda code into binary, and TensorFlow does not have this problem. In addition, the import of Theano consumes

protected] .Caffe Simple and fast.CNTK Simple and fast.TensorFlow uses only CUDNN v2, but even so its performance is still 1.5 times times slower than V2 with CUDNN torch, and there is a memory overflow problem in training googlenet when the batch size is 128.Theano performance on large networks is comparable to TORCH7. But its main problem is that the boot time is particularly long because it needs to compile the C/cuda code into binary, and TensorFlow does not have this problem. In addition,

CNN operation, the calculation is still very large, many of which are in fact repeated calculation; SVM model: And it is a linear model, it is obviously not the best choice when labeling data is not missing; Training test is divided into multiple steps: Regional nomination, feature extraction, classification, regression are disconnected training process, intermediate data also need to be saved separately; The space and time cost of training is high: the characteristics of the convol

1. Preface AI is a current hot topic, from the current Google's Alphago to smart cars, artificial intelligence has entered all aspects of our lives. Machine learning is a method of implementing artificial intelligence, which uses algorithms to analyze data, then learn from it, and finally make predictions and decisions about reality. Deep learning, however, is a

independent methods working in parallel. This may be your last step, a fancy step.Editorial review: Xavier Amatriain does not recommend deep learning as a general-purpose algorithm, and cannot be said to be because deep learning is not good, but because deep

know what to choose a way to win the game. At this point, you may realize that it's always easy to get things done with an integrated approach. Of course the only problem with integration is the need to keep all independent methods working in parallel. This may be your last step, a fancy step.Editorial review: Xavier Amatriain does not recommend deep learning as a general-purpose algorithm, and cannot be s

learning.If you want a simple learning Version. Then you can look at the following list:Mathematical Foundations (especially calculus, probability and linear algebra)Python BasicsStatistical basisMachine Learning Basicssuggested time:2-6 monthsStep 1: machine configurationBefore you proceed to the next step, you should make sure that you have a hardware environment that supports your

1979, in the book "Del, Escher, Bach-set the Big one". Hofstadter's PhD, Harry Foundalis, established an automated system to solve his doctoral research project, a system called "Phaeco". This program can not only solve bongard problem, but also is a kind of architecture of cognitive visual pattern recognition. deep Learning and Bongard issues The Phaeco created in 2006 is very influential because it not

Computational Network Toolkit (CNTK) is a Microsoft-produced open-Source Deep learning ToolkitUsing CNTK to engage in deep learning (a) Getting StartedComputational Network Toolkit (CNTK) is a Microsoft-produced open-source deep learning