============================================================================================================ `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
PyTorch implementation for ACL 2021 paper "Maria: A Visual Experience Powered Conversational Agent".
Maria: A Visual Experience Powered Conversational Agent This repository is the Pytorch implementation of our paper "Maria: A Visual Experience Powered
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"
DSPoint Official implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion". Paper link: https://arxiv.org/abs/2111.10
学习 python3 以来写的一些垃圾玩具……
和东哥做兄弟 Author: chiupam 版权 未经本人同意,仓库内所有资源文件,禁止任何公众号、自媒体、开发者进行任何形式的转载、发布、搬运。 声明 这不是一个开源项目,只是把 GitHub 当作一个代码的存储空间,本项目不接受任何开源要求。 仅用于学习研究,禁止用于商业用途,不能保证其合法性
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.
Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal
Image Segmentation using U-Net, U-Net with skip connections and M-Net architectures
Brain-Image-Segmentation Segmentation of brain tissues in MRI image has a number of applications in diagnosis, surgical planning, and treatment of bra
GAN-based Matrix Factorization for Recommender Systems
GAN-based Matrix Factorization for Recommender Systems This repository contains the datasets' splits, the source code of the experiments and their res
Implementation of Online Label Smoothing in PyTorch
Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
The official implementation of NeurIPS 2021 paper: Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
Blender scripts for computing geodesic distance
GeoDoodle Geodesic distance computation for Blender meshes Table of Contents Overivew Usage Implementation Overview This addon provides an operator fo
Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US simulation
AutomaticUSnavigation Investigating automatic navigation towards standard US views integrating MARL with the virtual US environment developed in CT2US
Building blocks for uncertainty-aware cycle consistency presented at NeurIPS'21.
UncertaintyAwareCycleConsistency This repository provides the building blocks and the API for the work presented in the NeurIPS'21 paper Robustness vi
This is the repository for The Machine Learning Workshops, published by AI DOJO
This is the repository for The Machine Learning Workshops, published by AI DOJO. It contains all the workshop's code with supporting project files necessary to work through the code.
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.
Official implementations of PSENet, PAN and PAN++.
News (2021/11/03) Paddle implementation of PAN, see Paddle-PANet. Thanks @simplify23. (2021/04/08) PSENet and PAN are included in MMOCR. Introduction
Code of the paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodner and Joachim Denzler
Part Detector Discovery This is the code used in our paper "Part Detector Discovery in Deep Convolutional Neural Networks" by Marcel Simon, Erik Rodne
CoRe: Contrastive Recurrent State-Space Models
CoRe: Contrastive Recurrent State-Space Models This code implements the CoRe model and reproduces experimental results found in Robust Robotic Control
This is the official source code of "BiCAT: Bi-Chronological Augmentation of Transformer for Sequential Recommendation".
BiCAT This is our TensorFlow implementation for the paper: "BiCAT: Sequential Recommendation with Bidirectional Chronological Augmentation of Transfor
Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays
Numbering permanent and deciduous teeth via deep instance segmentation in panoramic X-rays In this repo, you will find the instructions on how to requ
DilatedNet in Keras for image segmentation
Keras implementation of DilatedNet for semantic segmentation A native Keras implementation of semantic segmentation according to Multi-Scale Context A
Python interface for the DIGIT tactile sensor
DIGIT-INTERFACE Python interface for the DIGIT tactile sensor. For updates and discussions please join the #DIGIT channel at the www.touch-sensing.org