Implementation of deep residual networks with inception bottleneck in Lasagne
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Updated
Jun 28, 2016 - Python
Implementation of deep residual networks with inception bottleneck in Lasagne
Python implementation of machine learning algorithms
Generating proportional fonts with deep learning
Adversarial Discriminative Domain Adaptation with MNIST 64x64 in Lasagne-Theano
Lasagne Implementation of Densely Connected Convolutional Networks
Boundary Equibilibrium Generative Adversarial Networks (BEGAN) in Lasagne-Theano
FlowNetS and FlowNetC port to Theano
This work is part of practical Deep Learning in Computer Vision in which we apply CNN(Convolutional Neural Network) to predict protein function in different classes. We used Theano and lasagne deep learning libraries for CNN
Dockerfiles for deep learning tools.
The repository contains example code for conditional Generative Adversarial Model in theno and Lasagne. The code follows the paper https://arxiv.org/abs/1411.1784 but with a different deep architecture. The folder Generative Images contains Some sampled generated images from the code.
Repository with CNN code base on Lasagne and nolearn
Bayesian dessert for Lasagne
Image Classification with Python, Theano and Lasagne.
Source code of the TUCMI submission to BirdCLEF2017
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