============================================================================================================ `MILA will stop developing Theano <https://groups.google.com/d/msg/theano-users/7Poq8BZutbY/rNCIfvAEAwAJ>`_. The PyMC developers are continuing Theano development in a `fork <https://github.com/pymc-devs/theano-pymc>`_. ============================================================================================================ To install the package, see this page: http://deeplearning.net/software/theano/install.html For the documentation, see the project website: http://deeplearning.net/software/theano/ Related Projects: https://github.com/Theano/Theano/wiki/Related-projects It is recommended that you look at the documentation on the website, as it will be more current than the documentation included with the package. In order to build the documentation yourself, you will need sphinx. Issue the following command: :: python ./doc/scripts/docgen.py Documentation is built into ``html/`` The PDF of the documentation can be found at ``html/theano.pdf`` ================ DIRECTORY LAYOUT ================ ``Theano`` (current directory) is the distribution directory. * ``Theano/theano`` contains the package * ``Theano/theano`` has several submodules: * ``gof`` + ``compile`` are the core * ``scalar`` depends upon core * ``tensor`` depends upon ``scalar`` * ``sparse`` depends upon ``tensor`` * ``sandbox`` can depend on everything else * ``Theano/examples`` are copies of the example found on the wiki * ``Theano/benchmark`` and ``Theano/examples`` are in the distribution, but not in the Python package * ``Theano/bin`` contains executable scripts that are copied to the bin folder when the Python package is installed * Tests are distributed and are part of the package, i.e. fall in the appropriate submodules * ``Theano/doc`` contains files and scripts used to generate the documentation * ``Theano/html`` is where the documentation will be generated
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.
Overview
smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectious disease models: the COVID-19 case by Storvik et al
smc.covid smc.covid is an R package related to the paper A sequential Monte Carlo approach to estimate a time varying reproduction number in infectiou
0 Oct 15, 2021
Learning and Building Convolutional Neural Networks using PyTorch
Image Classification Using Deep Learning Learning and Building Convolutional Neural Networks using PyTorch. Models, selected are based on number of ci
126 Dec 22, 2022
Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder
Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder Authors: - Eashan Adhikarla - Dan Luo - Dr. Brian D. Davison Abstract Many
4 Dec 25, 2022
This repository contains tutorials for the py4DSTEM Python package
py4DSTEM Tutorials This repository contains tutorials for the py4DSTEM Python package. For more information about py4DSTEM, including installation ins
11 Dec 23, 2022
An introduction to satellite image analysis using Python + OpenCV and JavaScript + Google Earth Engine
A Gentle Introduction to Satellite Image Processing Welcome to this introductory course on Satellite Image Analysis! Satellite imagery has become a pr
32 Jan 03, 2023
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"
DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is
2 Dec 15, 2022
Pixel-Perfect Structure-from-Motion with Featuremetric Refinement (ICCV 2021, Oral)
Pixel-Perfect Structure-from-Motion (ICCV 2021 Oral) We introduce a framework that improves the accuracy of Structure-from-Motion by refining keypoint
831 Dec 29, 2022
Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity
[ICLR 2022] Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity by Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elen
18 Dec 31, 2022
PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks
Dynamic Data Augmentation with Gating Networks This is an official PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks
3 Oct 26, 2022
GyroSPD: Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices
GyroSPD Code for the paper "Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices" accepted at NeurIPS 2021. Re
12 Dec 12, 2022
I created My own Virtual Artificial Intelligence named genesis, He can assist with my Tasks and also perform some analysis,,
Virtual-Artificial-Intelligence-genesis- I created My own Virtual Artificial Intelligence named genesis, He can assist with my Tasks and also perform
1 Nov 05, 2021
Breaching - Breaching privacy in federated learning scenarios for vision and text
Breaching - A Framework for Attacks against Privacy in Federated Learning This P
139 Jan 03, 2023
Automatic library of congress classification, using word embeddings from book titles and synopses.
Automatic Library of Congress Classification The Library of Congress Classification (LCC) is a comprehensive classification system that was first deve
3 Oct 01, 2022
Breast-Cancer-Prediction
Breast-Cancer-Prediction Trying to predict whether the cancer is benign or malignant using REGRESSION MODELS in Python. Team Members NAME ROLL-NUMBER
3 Feb 18, 2022
The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".
Block Modeling-Guided Graph Convolutional Neural Networks This repository contains the demo code of the paper: Block Modeling-Guided Graph Convolution
22 Dec 08, 2022
Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks
Introduction This repository contains the modified caffe library and network architectures for our paper "Automated Melanoma Recognition in Dermoscopy
47 Nov 24, 2022
Medical image analysis framework merging ANTsPy and deep learning
ANTsPyNet A collection of deep learning architectures and applications ported to the python language and tools for basic medical image processing. Bas
118 Dec 24, 2022
ANN model for prediction a spatio-temporal distribution of supercooled liquid in mixed-phase clouds using Doppler cloud radar spectra.
VOODOO Revealing supercooled liquid beyond lidar attenuation Explore the docs » Report Bug · Request Feature Table of Contents About The Project Built
2 Apr 28, 2022
SafePicking: Learning Safe Object Extraction via Object-Level Mapping, ICRA 2022
SafePicking Learning Safe Object Extraction via Object-Level Mapping Kentaro Wad
49 Oct 24, 2022
[ICCV-2021] An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation
An Empirical Study of the Collapsing Problem in Semi-Supervised 2D Human Pose Estimation (ICCV 2021) Introduction This is an official pytorch implemen
42 Jan 04, 2023