Search Results for "hash-linux" - Page 11
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Showing 260 open source projects for "hash-linux"
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Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only... -
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. We've created some example projects to demonstrate the usage of Swift AI. ... -
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Neural Enhance
Super Resolution for images using deep learning
Neural Enhance is an archived deep-learning project for increasing image resolution through neural super-resolution. It can generate enlarged images at 2x or potentially higher scales by synthesizing details learned from training images. The generated details are plausible rather than exact reconstructions of information missing from the original picture. Pretrained models support photo enhancement and JPEG artifact repair. Users can also train custom networks with perceptual and adversarial... -
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Convolution arithmetic
A technical report on convolution arithmetic in deep learning
A technical report on convolution arithmetic in the context of deep learning. The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory. We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures.... -
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Neural Doodle
Turn your two-bit doodles into fine artworks with deep neural networks
Neural Doodle is an archived creative AI project that implements semantic style transfer with deep neural networks. It can transform simple annotated sketches into detailed images by borrowing visual characteristics from reference artwork or photographs. Semantic maps identify corresponding regions so style patches can be transferred to appropriate parts of a generated image. The software also performs conventional style transfer when semantic annotations are omitted. Additional modes... -
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ai trading
The aim is to build a prototype of agent able to trade by himself
The aim is to build a prototype of agent able to trade by himself selecting his own criterias and strategies based on its AI capability. Deep learning AI autonomous agent to build FX trading strategies -
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evoNet
Deep learning for population genetic inference
This software implements the algorithms described in the following paper: Sheehan, S. and Song, Y.S. Deep learning for population genetic inference. PLoS Computational Biology, in press. -
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Awesome Recurrent Neural Networks
A curated list of resources dedicated to RNN
A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn... -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and... -
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Monk Computer Vision
A low code unified framework for computer vision and deep learning
Monk is an open source low code programming environment to reduce the cognitive load faced by entry level programmers while catering to the needs of Expert Deep Learning engineers. There are three libraries in this opensource set. - Monk Classiciation- https://monkai.org. A Unified wrapper over major deep learning frameworks. Our core focus area is at the intersection of Computer Vision and Deep Learning algorithms. - Monk Object Detection -...
