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brain.js is a library of Neural Networks written in JavaScript. π‘ Note: This is a continuation of the harthur/brain repository (which is not maintained anymore). For more details, check out this issue.

neural-network brain recurrent-neural-networks easy-to-use api web nodejs browser convolutional-neural-networks node stream ai artificial-intelligence brainjs brain.js feed-forward classifier neural network neural-networks machine-learning synapse recurrent long-short-term-memory gated-recurrent-unit rnn lstm gruIntel 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 performanceAiLearning: ζΊε¨ε¦δΉ - MachineLearning - MLγζ·±εΊ¦ε¦δΉ - DeepLearning - DLγθͺηΆθ―θ¨ε€η NLP

fp-growth apriori mahchine-leaning naivebayes svm adaboost kmeans svd pca logistic regression recommendedsystem sklearn scikit-learn nlp deeplearning dnn lstm rnnThis 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 odeMulti-layer Recurrent Neural Networks (LSTM, RNN) for word-level language models in Python using TensorFlow. Mostly reused code from https://github.com/sherjilozair/char-rnn-tensorflow which was inspired from Andrej Karpathy's char-rnn.

rnn tensorflow rnn-tensorflow lstmIn 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-networkCompared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

machine-learning deep-learning lstm human-activity-recognition neural-network rnn recurrent-neural-networks tensorflow activity-recognitionν 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 inceptionThis repository contains the iPython notebook and training data to accompany the O'Reilly tutorial on sentiment analysis with LSTMs in Tensorflow. See the original tutorial to run this code in a pre-built environment on O'Reilly's servers with cell-by-cell guidance, or run these files on your own machine. There is also another file called Pre-Trained LSTM.ipynb which allows you to input your own text, and see the output of the trained network. Before running the notebook, you'll first need to download all data we'll be using. This data is located in the models.tar.gz and training_data.tar.gz tarballs. We will extract these into the same directory as Oriole LSTM.ipynb. As always, the first step is to clone the repository.

sentiment-analysis tensorflow lstm rnnTensorflow implementation of attention mechanism for text classification tasks. Inspired by "Hierarchical Attention Networks for Document Classification", Zichao Yang et al. (http://www.aclweb.org/anthology/N16-1174).

attention tensorflow rnn text-classification sentiment-analysisChatbot in 200 lines of code using TensorLayer

tensorlayer tensorflow chatbot rnn lstm bot nlp chat corpusA vanilla sequence to sequence model presented in https://arxiv.org/abs/1409.3215, https://arxiv.org/abs/1406.1078 consits of using a recurrent neural network such as an LSTM (http://dl.acm.org/citation.cfm?id=1246450) or GRU (https://arxiv.org/abs/1412.3555) to encode a sequence of words or characters in a source language into a fixed length vector representation and then deocoding from that representation using another RNN in the target language. An extension of sequence to sequence models that incorporate an attention mechanism was presented in https://arxiv.org/abs/1409.0473 that uses information from the RNN hidden states in the source language at each time step in the deocder RNN. This attention mechanism significantly improves performance on tasks like machine translation. A few variants of the attention model for the task of machine translation have been presented in https://arxiv.org/abs/1508.04025.

pytorch seq2seq deep-learning rnnIt is assumed that you already have training and test data. The data is made from many examples (I'm using 684K examples), each example is made from the text from the start of the article, which I call description (or desc), and the text of the original headline (or head). The texts should be already tokenized and the tokens separated by spaces. Once you have the data ready save it in a python pickle file as a tuple: (heads, descs, keywords) were heads is a list of all the head strings, descs is a list of all the article strings in the same order and length as heads. I ignore the keywrods information so you can place None.

keras nlp generation summarization rnnCS224n: Natural Language Processing with Deep Learning Assignments Winter, 2017

cs224n deep-learning natural-language-processing word2vec rnn tensorflowRecurrent neural network for audio noise reduction

rnn noise-reduction audioRNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above version. This page introduces what is RNNSharp, how it works and how to use it. To get the demo package, you can access release page.

rnn crf deep-learning machine-learning c-sharp sequence-labeling rnn-model recurrent-neural-networks nlp lstmTo examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. Download the Dataset needed for running the code from here.

machine-learning supervised-learning stock-price-forecasting forecasting rnn lstm lstm-neural-networks video concept-video analysis
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