• – CPU/GPU symbolic expression compiler in python (from MILA lab at University of Montreal) • – provides a Matlab-like environment for state-of-the-art machine learning algorithms in lua (from Ronan Collobert, Clement Farabet and Koray Kavukcuoglu) • – Pylearn2 is a library designed to make machine learning research easy. • – A Theano framework for training neural networks • – TensorFlow™ is an open source software library for numerical computation using data flow graphs. • – MXNet is a deep learning framework designed for both efficiency and flexibility.
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• -Caffe is a deep learning framework made with expression, speed, and modularity in mind.Caffe is a deep learning framework made with expression, speed, and modularity in mind. • – Lasagne is a lightweight library to build and train neural networks in Theano. • – A theano based deep learning library. • – examples of how to do Deep Learning with Theano (from LISA lab at University of Montreal) • – A GPU based Neural Network Framework • – Matlab Deep Learning Tools • Computational Network Toolkit – is a unified deep-learning toolkit by Microsoft Research. • – A MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications.
It is simple, efficient, and can run and learn state-of-the-art CNNs. • – A Matlab toolbox for Deep Learning (from Rasmus Berg Palm) • BigDL. A distributed open-source Apache Spark deep learning library designed for efficient scale-out to multiple nodes. CPU-optimized via MKL. Scala and Python support. (Developed and supported by Intel Corp). • – A fast C++/CUDA implementation of convolutional (or more generally, feed-forward) neural networks.
AtenaKid / Caffe_Matlab. Pull requests 0. Projects 0 Insights Dismiss Join GitHub today. GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together. This project contains example of Matlab interface for Caffe (known as Matcaffe). Currently, the. Hello, I wanted to use Convolutional neural networks in Matlab for a project of mine. Can you suggest certain easy to use toolboxes. And also are there any good tutorials on how to use caffe using matlab.
It can model arbitrary layer connectivity and network depth. Any directed acyclic graph of layers will do. Training is done using the back-propagation algorithm. Matlab code for learning Deep Belief Networks (from Ruslan Salakhutdinov).
• – Tomas Mikolov’s Recurrent Neural Network based Language models Toolkit. • -RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. Simplified version of Ruslan Salakhutdinov’s code, by Andrej Karpathy (Matlab).