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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 gruMost of the codes are simple refactorings of Aymeric Damien's Tutorial or Nathan Lintz's Tutorial. There could be missing credits. Please let me know.

tensorflow-tutorials convolutional-neural-networks recurrent-neural-networksCompared 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-recognitionThe goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format).

deep-learning tensorflow reinforcement-learning machine-learning pattern-recognition object-detection convolutional-neural-networks recurrent-neural-networks neural-networkSOD is an embedded, modern cross-platform computer vision and machine learning software library that expose a set of APIs for deep-learning, advanced media analysis & processing including real-time, multi-class object detection and model training on embedded systems with limited computational resource and IoT devices. SOD was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in open source as well commercial products.

computer-vision library deep-learning image-processing object-detection cpu real-time convolutional-neural-networks recurrent-neural-networks face-detection facial-landmarks machine-learning-algorithms image-recognition image-analysis vision-framework embedded detection iot-device iotThis repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code.

deep-learning tensorflow-examples convolutional-neural-networks recurrent-neural-networks autoencoder gan style-transfer natural-language-processing machine-translationSome 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 cnnPlease use python 2.7 to install LSTMVis.

lstm neural-network visualization recurrent-neural-networksThis repository holds the code to a new kind of RNN model for processing sequential data. The model computes a recurrent weighted average (RWA) over every previous processing step. With this approach, the model can form direct connections anywhere along a sequence. This stands in contrast to traditional RNN architectures that only use the previous processing step. A detailed description of the RWA model has been published in a manuscript at https://arxiv.org/pdf/1703.01253.pdf. Because the RWA can be computed as a running average, it does not need to be completely recomputed with each processing step. The numerator and denominator can be saved from the previous step. Consequently, the model scales like that of other RNN models such as the LSTM model.

recurrent-neural-networks sequential-data time-series research rwa-model recurrent-weighted-average deep-memoryTrending deep learning Github repositories can be found here. Hint: This will be updated regularly.

deep-learning deep-neural-networks deep-reinforcement-learning convolutional-neural-networks recurrent-neural-networks stargazers-count artificial-neural-networks artificial-intelligence machine-learning top-repositoriesImplementation of the handwriting synthesis experiments in the paper Generating Sequences with Recurrent Neural Networks by Alex Graves. The implementation closely follows the original paper, with a few slight deviations, and the generated samples are of similar quality to those presented in the paper. Currently, the Hand class must be imported from demo.py. If someone would like to package this project to make it more usable, please contribute.

handwriting-synthesis handwriting-generation recurrent-neural-networks tensorflowThe objective is to predict continuous values, sin and cos functions in this example, based on previous observations using the LSTM architecture. This example has been updated with a new version compatible with the tensrflow-1.1.0. This new version is using a library polyaxon that provides an API to create deep learning models and experiments based on tensorflow.

lstm tensorflow recurrent-networks deep-learning sequence-prediction tensorflow-lstm-regression jupyter time-series recurrent-neural-networksThis repository contains an independent TensorFlow implementation of recurrent entity networks from Tracking the World State with Recurrent Entity Networks. This paper introduces the first method to solve all of the bAbI tasks using 10k training examples. The author's original Torch implementation is now available here. Percent error for each task, comparing those in the paper to the implementation contained in this repository.

tensorflow recurrent-neural-networks deep-learning machine-learning natural-language-processingRNNSharp 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 lstmKomputation is a neural network framework for the JVM written in the Kotlin programming language.

neural-networks framework jvm kotlin nlp machine-learning convolutional-neural-networks recurrent-neural-networks seq2seq artificial-intelligence cuda gpu nvidiaCURRENTLY NOT WORKING! There will be a further notice when it's updated.NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here.

neat neuroevolution neural-network topologies reinforcement-learning recurrent-neural-networks genetic-algorithm machine-learning go-librarySingle- and multilayer LSTM networks with no additional output nonlinearity based on aymericdamien's TensorFlow examples and Sequence prediction using recurrent neural networks. Experiments with varying numbers of hidden units, LSTM cells and techniques like gradient clipping were conducted using static_rnn and dynamic_rnn. All networks have been optimized using Adam on the MSE loss function.

tensorflow lstm recurrent-neural-networks neural-network timeseries prediction experiment gruThe goal of this project is to generate completely new original lyrics inspired by the work of an arbitrary number of artists. Try it yourself by running example.sh with your own data.

deep-learning recurrent-neural-networks tensorflow natural-language-processing language-modelingThis repository contains the code implemented for my Master's thesis project submitted in fulfillment of the requirements for the degree of Master of Science at the IT University of Copenhagen supervised by Professor Sebastian Risi. The following software is shared for educational purpose only. The author of the code and its affiliated institution are not responsible in any manner whatsoever for any damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of the use or inability to use this software. Neither the names of the author or the name of its affiliated institution may be used to endorse or promote products derived from this software. Please find more details in the provided Licence file.

deep-learning data-science neural-networks wearable-devices privacy security recurrent-neural-networksAn implementation of zoneout regularizer on LSTM-RNN by Tensorflow

tensorflow recurrent-neural-networks deep-neural-networks
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