Deep Learning Training Scripts With Python

Last update: Dec 15, 2022

Overview

Deep Learning Training Scripts

DNN Frameworks

Caffe
PyTorch
Tensorflow

CNN Models

VGG
ResNet
DenseNet
Inception

Language Modeling

GatedCNN-LM
Attention-MLP
Attention-RNN

Generative Model

DCGAN
A3C

RNN Models

DeepSpeech2

Dependencies

Pytorch 1.1.0
Tensorflow 1.13.1
cuda-toolkit 10.0
cudnn 7.4.2

Polybench

Modified to measure performance and energy consumption of soff. Run make in each folder to compile and run.

Hioki3334

A small python code snippet to control HIOKI 3334 AC/DC Power HiTESTER.

Owner

Multicore Computing Research Lab

GitHub Repository

Resilient projection-based consensus actor-critic (RPBCAC) algorithm

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Implementation of "Deep Implicit Templates for 3D Shape Representation"

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Python-based Informatics Kit for Analysing Chemical Units

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3D ResNet Video Classification accelerated by TensorRT

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a reimplementation of Holistically-Nested Edge Detection in PyTorch

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Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

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Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

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Geometric Sensitivity Decomposition

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Python port of R's Comprehensive Dynamic Time Warp algorithm package

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A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

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EEGEyeNet is benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty

Introduction EEGEyeNet EEGEyeNet is a benchmark to evaluate ET prediction based on EEG measurements with an increasing level of difficulty. Overview T

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Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

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Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

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RITA is a family of autoregressive protein models, developed by LightOn in collaboration with the OATML group at Oxford and the Debora Marks Lab at Harvard.

RITA: a Study on Scaling Up Generative Protein Sequence Models RITA is a family of autoregressive protein models, developed by a collaboration of Ligh

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