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April 1, 2017

DeepLearningKit

DeepLearningKit is an Open Source with Apache 2.0 License. It is a Deep Learning Framework for Apple’s iOS, OS X and tvOS that is available at github.com/DeepLearningKit/DeepLearningKit. The goal is to support using pre-trained Deep Learning models on all Apple’s devices that have GPU(s). It is developed in Swift to easily run on all platforms such as iOS, OS X and tvOS and Metal to efficiently use on-device GPU to ensure low-latency Deep Learning calculations.DeepLearningKit currently supports using (Deep) Convolutional Neural Networks, such as for image recognition, trained with the Caffe Deep Learning Framework but the long term goal is to support [...]

9.5
 
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April 1, 2017

Torch

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It offers an easy to use and efficient program to its users, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. Users of Torch will be able to take advantage of its core features such as a powerful N-dimensional array, lots of routines for indexing, slicing, transposing, amazing interface to C, via LuaJIT, linear algebra routines, neural network, and energy-based models, numeric optimization routines, fast and efficient GPU support and embeddable, with ports to iOS, Android and FPGA back ends.The [...]

7.25
 
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April 1, 2017

MLPNeuralNet

MLPNeuralNet is a fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples through trained neural networks. It is built on top of Apple’s Accelerate Framework using vectored operations and hardware acceleration (if available). MLPNeuralNet is for users who have engineered a prediction model using Matlab (Python or R) and would like to use it in an iOS application. In that case, MLPNeuralNet is exactly what is needed. MLPNeuralNet is designed to load and run models in forward propagation mode only. Some of the features that users will be able to take advantage of MLPNeuralNet would be Classification, [...]

4
 
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April 1, 2017

Knet

Knet is the Koç University deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia. Knet uses dynamic computational graphs generated at runtime for automatic differentiation of (almost) any Julia code. This allows machine learning models to be implemented by defining just the forward calculation using the full power and expressivity of Julia. The implementation can use helper functions, loops, conditionals, recursion, closures, tuples and dictionaries, array indexing, concatenation and other high level language features, some of which are often missing in the [...]

3.25
 
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April 1, 2017

Mocha

Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe. Efficient implementations of general stochastic gradient solvers and common layers in Mocha could be used to train deep / shallow (convolutional) neural networks, with (optional) unsupervised pre-training via (stacked) auto-encoders. Mocha has a clean architecture with isolated components like network layers, activation functions, solvers, regularizers, initializers, etc. Built-in components are sufficient for typical deep (convolutional) neural network applications and more are being added in each release. All of them could be easily extended by adding custom sub-types. [...]

3.75
 
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April 1, 2017

deeplearn-rs

Deeplearn-rs is a deep neural network in Rust. It has currently implemented models such as MNIST handwritten digit recognition and char-rnn using LSTM. Layers that has already been implemented are Matrix multiply (fully connected), Add (for bias, for example), LSTM, Softmax, MSE loss and Cross entropy loss. deeplearn-rs have SGD and RMSProp as optimizers.Deeplearn-rs provide utilities for working with data which includes images, TSV and CSV and raw text data and word embeddings. It also allows data types other than f32 and implements casting between arrays of primitive numeric types as well as more layer types such as Conv2d, Pooling and [...]

3.25
 
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April 1, 2017

NVIDIA DIGITS

The NVIDIA DIGITS puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks without the need to write code. NVIDIA DIGITS simplifies common deep learning tasks for its users such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations and selecting the best performing model from the results browser for deployment. NVIDIA DIGITS is completely interactive so that data scientists can focus on designing [...]

8.25
 
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April 1, 2017

NeuralTalk2

NeuralTalk2 is an efficient image captioning code in Torch that runs on GPU. Compared to the original NeuralTalk this implementation is batched, uses Torch, runs on a GPU, and supports CNN fine-tuning. All of these put together results in quite a large increase in training speed for the Language Model (~100 x). Image Captioning aims to describe photos with better accuracy. For users who just wanted to caption images, there would be a need to run the evaluation script on a pre-trained model checkpoint. This pre-trained checkpoint can be downloaded. Auto image captioning seems to be an impossible thing for AI years ago. But since it has been made open [...]

5.75
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