pytorch keras

) SOURCE Link: https://github.com/PAIR-code/deeplearnjs 7. Fast style migration base based on TensorFlow (GitHub 4843 stars, contributors are Logan Engstrom of MIT) SOURCE Link: Https://github.com/lengstrom/fast-style-transfer 8. PYSC2: StarCraft 2 Learning Environment (GitHub 3684 stars, contributors are DeepMind Timo Ewalds) SOURCE Link: https://github.com/deepmind/pysc2 9. Airsim:microsoft AI Research Open source Simulator based on Unreal Engine for automatic driving (GitHub 3861 star, contr

Probabilistic programming based on Python and Pytorch (GitHub 2387 stars, contributor Uber Engineering) SOURCE Link: Https://github.com/uber/pyro 19.iGAN: Gan-based interactive image generation (GitHub 2369 stars) Source Address: Https://github.com/junyanz/iGAN 20.deep-image-prior: Recovering images with a neural network (GitHub 2188 stars, contributors are Skoltech Dmitry Ulyanov, Ph.D) Source Address: Https://github.com/DmitryUlyanov/deep-image-p

Image recognition is the mainstream application of deep learning today, and Keras is the easiest and most convenient deep learning framework for getting started, so you have to emphasize the speed of the image recognition and not grind it. This article allows you to break through five popular network structures in the shortest time, and quickly reach the forefront of image recognition technology. Author | Adrian RosebrockTranslator | Guo Hongguan

powerful and flexible data frames of R into Python. For natural Language Processing (NLP), you can use the prestigious NLTK and lightning-fast spacy. For machine learning, there is an actual combat test of Scikit-learn. When it comes to deep learning, all the current libraries (Tensorflow,pytorch,chainer,apache Mxnet,theano, etc.) are the first projects implemented on Python.(on liveedu, a German AI developer teaches you how to develop two simple mac

efficient. An obvious trend is the use of modular structure, which can be seen in googlenet and ResNet, this is a good design example, the use of modular structure can reduce the design of our network space, and another point is that the use of bottlenecks in the module can reduce the computational capacity, which is also an advantage. This article does not mention some of the recent mobile-based lightweight CNN models, such as mobilenet,squeezenet,shufflenet, which are very small in size, and

this type of model. 3.3) Support Vector Machine (SVM) For more information about SVM, see Professor Andrew Ng's CS229 on Coursera. (If you have the ability, visit the original CS229 on youtube or Netease open course ). The svm API documentation is very well-developed, and it is not very difficult to adjust the bag. However, in most data mining competitions (such as kaggle), SVM is often inferior to xgboost. 3.4) Neural Network) Compared with the industry's top Neural Network Libraries (such a

Algorithmic/Data engineer essential Skills Basic knowledge Linear algebra Matrix theory Probability theory Stochastic process Graph theory Numerical analysis Optimization theory Machine learning Statistical learning methods Data mining Platform Linux Language Python Linux Shell Base Library NumPy Pandas Sklearn SciPy Matplotlib or Seaborn

current classification method is the number of hidden layers to distinguish whether "depth". When the number of hidden layers in a neural network reaches more than 3 layers, it is called "deep neural Network" or "deep learning".Uh deep learning, it turns out to be so simple.If you have time, you are advised to play more in this playground. You will soon have a perceptual understanding of neural networks and deep learning.FrameworkThe engine behind the playground is Google's deep learning framew

1. Bachelor degree or above, 2 years experience in image-based algorithm development;2. Good command of C + +, familiar with Python parallel development, interface development;3. Familiar with SVM, CNN, SSD, YOLOv2 lamp machine learning model, master the basis of digital image processing4. Familiar with at least one mainstream deep learning algorithm framework (e.g. Caffe,caffe2,mxnet,pytorch,tensorflow,keras