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Neural Network for Kotlin

https://github.com/cbeust/nnkTags | kotlin neural-network |

Implementation | Kotlin |

License | Public |

Platform |

Komputation 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 nvidiaScaleChain source code is under construction. Big changes are to come to integrate distributed neural network. The latest release does not include the integration of distributed neural network yet. ScaleChain is an altcoin that realizes distributed neural networks on blockchain.

This repository contains the implementation of a convolutional neural network used to segment blood vessels in retina fundus images. This is a binary classification task: the neural network predicts if each pixel in the fundus image is either a vessel or not. The neural network structure is derived from the U-Net architecture, described in this paper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Also on the STARE datasets, this method reports one of the best performances. The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels. A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images. Although the patches overlap, i.e. different patches may contain same part of the original images, no further data augmentation is performed. The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).

Example scripts for a deep, feed-forward neural network have been written from scratch. No machine learning packages are used, providing an example of how to implement the underlying algorithms of an artificial neural network. The code is written in the Julia, a programming language with a syntax similar to Matlab. The neural network is trained on the MNIST dataset of handwritten digits. On the test dataset, the neural network correctly classifies 98.42 % of the handwritten digits. The results are pretty good for a fully connected neural network that does not contain a priori knowledge about the geometric invariances of the dataset like a Convolutional Neural Network would.

Neural Complete is autocomplete based on a generative LSTM neural network, trained not only by python code but also on python source code. Ironically, it is trained on files containing keras imports. The result is a neural network trained to help writing neural network code.

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks of much greater complexity. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to computer vision with Convolution Neural Networks (CNN), time series analysis with Long Short-Term Memory (LSTM), classic neural network structures and application to computer security. High Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction mathematical foundations. Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this course; however, familiarity of at least one programming language is assumed. This course will be delivered in a hybrid format that includes both classroom and online instruction. This syllabus presents the expected class schedule, due dates, and reading assignments. Download current syllabus.

neural-network machine-learning tensorflow keras deeplearningDistiller is an open-source Python package for neural network compression research. Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.

pytorch pruning quantization pruning-structures jupyter-notebook network-compression deep-neural-networks regularization group-lassoI used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.

neural network apple playground ipad ai machine-learning artificial-intelligenceThis project uses iOS Playgrounds to display a working neural network. No external libraries are used. The matrix and neural network code is written in pure Swift. This playground has only been tested on an 12.9‑inch iPad Pro.

wwdc-scholarship computer-vision neural-network swift-playgroundsTinn (Tiny Neural Network) is a 200 line dependency free neural network library written in C99. The training data consists of hand written digits written both slowly and quickly. Each line in the data set corresponds to one handwritten digit. Each digit is 16x16 pixels in size giving 256 inputs to the neural network.

tiny neural network ansi feed forward back propagationNeural Network Basic contain implementation of simple and effective implementation of neural network. Functionality it is developed in C++ native programming language, with use STL and Visual Studio C++ Express 2010.

neural-network neural-networksFast Artificial Neural Network (FANN) Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast.

fann neural-network libraryLightweight backpropagation neural network in C. Intended for programs that need a simple neural network and do not want needlessly complex neural network libraries. Includes example application that trains a network to recognize handwritten digits.

In 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-learningSynaptic is a javascript neural network library for node.js and the browser, its generalized algorithm is architecture-free, so you can build and train basically any type of first order or even second order neural network architectures. This library includes a few built-in architectures like multilayer perceptrons, multilayer long-short term memory networks (LSTM), liquid state machines or Hopfield networks, and a trainer capable of training any given network, which includes built-in training tasks/tests like solving an XOR, completing a Distracted Sequence Recall task or an Embedded Reber Grammar test, so you can easily test and compare the performance of different architectures.

neural-network machine-learning long-short-term-memory perceptron architecture-freeDigit recognition contain implementation of simple and effective implementation of neural network. Neural network is used to recognize handwritten digits - OCR system. Core functionality it is developed in C++ native programming language, STL, boost, GUI in C++ .NET.

neural-network ocr recognitionPyTorch-NLP, or torchnlp for short, is a library of neural network layers, text processing modules and datasets designed to accelerate Natural Language Processing (NLP) research. Join our community, add datasets and neural network layers! Chat with us on Gitter and join the Google Group, we're eager to collaborate with you.

pytorch nlp natural-language-processing pytorch-nlp torchnlp data-loader embeddings word-vectors deep-learning dataset metrics neural-network sru machine-learningThis is an implementation of a fully connected neural network in NumPy. By using the matrix approach to neural networks, this NumPy implementation is able to harvest the power of the BLAS library and efficiently perform the required calculations. The network can be trained by a wide range of learning algorithms. Visit the project page or Read the documentation.

A flexible neural network library for Node.js and the browser. Check out a live demo of a movie recommendation engine built with Mind. Use plugins created by the Mind community to configure pre-trained networks that can go straight to making predictions.

mind neural-network prediction machine-learning learning sigmoid network neuron neural nnThis is a generative art project I made for my high school's programming club - which I'm the president/founder of I was the president/founder of until I graduated the other month. It's a neural network that has been trained on Kanye West's discography, and can use any lyrics you feed it and write a new song word by word that rhymes and has a flow (to an extent).

neural-network lyrics songs rhymes machine learning rap rap-songs generative-art songwriting mp3
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