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It is the generic golden program for deep learning with TensorFlow.Following are the supported features.

tensorflow tfrecords libsvm csv deep-learning machine-learning mlp cnn lstm classifier recommendation-system cpp spark grpc android mavenCode examples for new APIs of iOS 10. Just build with Xcode 8.

ios ios10 swift-3 swift-4 speech metal cnn image-recognition convolutional-neural-networks demo metal-performance-shaders metal-cnn uiviewpropertyanimatorIntel MKL-DNN repository migrated to https://github.com/intel/mkl-dnn. The old address will continue to be available and will redirect to the new repo. Please update your links. Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) is an open source performance library for deep learning applications. The library accelerates deep learning applications and framework on Intel(R) architecture. Intel(R) MKL-DNN contains vectorized and threaded building blocks which you can use to implement deep neural networks (DNN) with C and C++ interfaces.

intel mkl-dnn deep-learning deep-neural-networks cnn rnn lstm c-plus-plus intel-architecture xeon xeon-phi atom core simd sse42 avx2 avx512 avx512-vnni performanceWe release various convolutional neural networks (CNNs) trained on Places365 to the public. Places365 is the latest subset of Places2 Database. There are two versions of Places365: Places365-Standard and Places365-Challenge. The train set of Places365-Standard has ~1.8 million images from 365 scene categories, where there are at most 5000 images per category. We have trained various baseline CNNs on the Places365-Standard and released them as below. Meanwhile, the train set of Places365-Challenge has extra 6.2 million images along with all the images of Places365-Standard (so totally ~8 million images), where there are at most 40,000 images per category. Places365-Challenge will be used for the Places2 Challenge 2016 to be held in conjunction with the ILSVRC and COCO joint workshop at ECCV 2016. The data Places365-Standard and Places365-Challenge are released at Places2 website.

cnn baseline-cnns'Openpose' for human pose estimation have been implemented using Tensorflow. It also provides several variants that have made some changes to the network structure for real-time processing on the CPU or low-power embedded devices. 2018.5.21 Post-processing part is implemented in c++. It is required compiling the part. See: https://github.com/ildoonet/tf-pose-estimation/tree/master/src/pafprocess 2018.2.7 Arguments in run.py script changed. Support dynamic input size.

deep-learning openpose tensorflow mobilenet pose-estimation convolutional-neural-networks neural-network image-processing human-pose-estimation embedded realtime cnn mobile ros robotics catkinTensorflow implementation of Character-Aware Neural Language Models. The original code of author can be found here. The current implementation has a performance issue. See #3.

tensorflow cnn lstm nlpThis chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

tensorflow tensorflow-cookbook linear-regression neural-network tensorflow-algorithms rnn cnn svm nlp packtpub machine-learning tensorboard classification regression kmeans-clustering genetic-algorithm odeHow simple is it to cause a deep neural network to misclassify an image if an attacker is only allowed to modify the color of one pixel and only see the prediction probability? Turns out it is very simple. In many cases, an attacker can even cause the network to return any answer they want. The following project is a Keras reimplementation and tutorial of "One pixel attack for fooling deep neural networks".

keras cnn cifar10 machine-learning tensorflow deep-learning neural-network imagenet image-processing nlpThis is a rough list of my favorite deep learning resources. It has been useful to me for learning how to do deep learning, I use it for revisiting topics or for reference. I (Guillaume Chevalier) have built this list and got through all of the content listed here, carefully. You might also want to look at Andrej Karpathy's new post about trends in Machine Learning research.

awesome awesome-list deep-learning machine-learning tensorflow lstm cnnUpdate 04/06/2017 Article "Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods" have been accepted for publication in Pattern Recogntion (Elsevier). The Deepgaze CNN head pose estimator module is based on this work. Update 22/03/2017 Fixed a bug in mask_analysis.py and almost completed a more robust version of the CNN head pose estimator.

convolutional-neural-networks motion-tracking color-detection face-detection skin-detection motion-detection head-pose-estimation human-computer-interaction histogram-comparison histogram-intersection cnn particle-filter saliency-mapSequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

pytorch ner sequence-labeling crf lstm-crf char-rnn char-cnn named-entity-recognition part-of-speech-tagger chunking neural-networks nbest lstm cnn batchSome interesting TensorFlow tutorials for beginners.

tensorflow tensorflow-tutorials lstm cnnThis is the code for the article 'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

keras deep-learning seq2seq encoder-decoder lstm floydhub machine-learning cnn cnn-keras jupyter-notebook jupyterIn these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.

neural-network pytorch-tutorial batch-normalization cnn rnn autoencoder pytorch regression classification batch tutorial dropout dqn reinforcement-learning gan generative-adversarial-network machine-learningIn these tutorials, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. All methods mentioned below have their video and text tutorial in Chinese. Visit θ«η¦ Python for more.

tensorflow tensorflow-tutorials gan generative-adversarial-network rnn cnn classification regression autoencoder deep-q-network dqn machine-learning tutorial dropout neural-networkSome examples require MNIST dataset for training and testing. Don't worry, this dataset will automatically be downloaded when running examples (with input_data.py). MNIST is a database of handwritten digits, for a quick description of that dataset, you can check this notebook.

recurrent-neural-networks convolutional-neural-networks deep-learning-tutorial tensorflow tensorlayer keras deep-reinforcement-learning tensorflow-tutorials deep-learning machine-learning notebook autoencoder multi-layer-perceptron reinforcement-learning tflearn neural-networks neural-network neural-machine-translation nlp cnnWe run this script under TensorFlow 1.4 and the TensorLayer 1.8.0+. π This repo will be moved to here (please star) for life-cycle management soon. More cool Computer Vision applications such as pose estimation and style transfer can be found in this organization.

tensorlayer tensorflow super-resolution gan cnn srgan vgg16 vgg19 vggν μνλ‘μ°λ₯Ό κΈ°μ΄λΆν° μμ©κΉμ§ λ¨κ³λ³λ‘ μ°μ΅ν μ μλ μμ€ μ½λλ₯Ό μ κ³΅ν©λλ€. ν μνλ‘μ° κ³΅μ μ¬μ΄νΈμμ μ κ³΅νλ μλ΄μμ λλΆλΆμ λ΄μ©μ λ€λ£¨κ³ μμΌλ©°, κ³΅μ μ¬μ΄νΈμμ μ κ³΅νλ μμ€ μ½λλ³΄λ€λ ν¨μ¬ κ°λ΅νκ² μμ±νμμΌλ―λ‘ μ½κ² κ°λ μ μ΅ν μ μμ κ² μ λλ€. λν, λͺ¨λ μ£Όμμ νκΈλ‘(!) λμ΄ μμ΅λλ€.

neural-network tensorflow mnist autoencoder rnn deep-learning tutorial chatbot seq2seq dqn word2vec cnn gan inceptionCellular Neural Networks (CNN) [wikipedia] [paper] are a parallel computing paradigm that was first proposed in 1988. Cellular neural networks are similar to neural networks, with the difference that communication is allowed only between neighboring units. Image Processing is one of its applications. CNN processors were designed to perform image processing; specifically, the original application of CNN processors was to perform real-time ultra-high frame-rate (>10,000 frame/s) processing unachievable by digital processors. This python library is the implementation of CNN for the application of Image Processing.

cellular neural-network cnn image-processing cnn-processors paper edge-detection corner-detection library cross-platform feedback computer-vision computer-science control
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