best nvidia gpu for deep learning

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,

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

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

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

The key of AI is machine learning, machine learning breakthrough is deep learning, artificial neural network.In 1956, in the Dartmouth Conference (Dartmouth conferences), computer scientists first introduced the term "AI", the AI was born, and in subsequent days AI became the "fantasy object" of the lab. Decades later,

most important thing to know about OpenAI is to understand the frontiers of AI research.What is the research direction of Ai's frontier?OpenAI raised three points:-Training Generative Models-Algorithms for inferring algorithms from data-New approaches to reinforcement learningSo what do these three categories represent, respectively?Deep generative ModelsThe first type is oriented to the generation model, the main task is to generate new information,

: deep learning has made great progress in vision and speech, attributed to the ability to automatically extract high level features. The current reinforcement learning successfully combines the results of deep learning, that is, DQN, to get breakthrough on Atari games.Howev

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

Target detection is a simple task for a person, but for a computer it sees an array of values of 0~255, making it difficult to directly get a high-level semantic concept for someone or a cat in the image, or the target to eat the area in the image. The target in the image may appear in any position, the shape of the target may have a variety of changes, the background of the image is very different ..., these factors lead to target detection is not an easy task to solve. Thanks to

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

Several application cases of R language H2O packageAuthor's message: Inspired to understand the H2O platform of some R language implementation, online has a H2O demo file. I post some cases here, and put some small examples of their own practice.About H2O platform long what kind, can see H2O's official website, about deep learning long what kind of, you can see some tutorials, such as PARALLELR blog in the

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 to practice, an indispensable path is to the intelligent terminal, embedded equipment and other directions. But the terminal device does not have the powerful performance of GPU server, how to make the end device application deep learning? Fortunately, Googl

Python to do deep learning caffe design CombatEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For learning difficulties do no

install-y Python-pip Recommendation:The installation process is best a command one command implementation, there was a mistake to facilitate timely discovery.Installation process has failed to install the situation, do not worry, usually because of network reasons, re-execute the command, generally try a few times will be good ~3. cuda8.0DownloadOfficial website Download: https://developer.nvidia.com/cuda-downloadsDirect download: cuda-repo-ubuntu1604-8-0-local_8.0.44-1_amd64.debInstallatio

Before I have been using Theano, the previous five deeplearning related articles are also learning Theano some notes, at that time already feel Theano use up a little trouble, sometimes want to achieve a new structure, it will take a lot of time to programming, so think about the code modularity, Easy to reuse, but because it's too busy to do it. Recently discovered a framework called Keras, which coincides with my ideas, is particularly simple to use