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deep-learning-papers

Deep learning is an AI function and a subset of machine learning, used for processing large amounts of complex data. Deep learning can automatically create algorithms based on data patterns.

Here are 13 public repositories matching this topic...

dnlcrl / PyScholarGraph

Code for "Five Hundred Deep Learning Papers, Graphviz and Python" (http://goo.gl/l1PIoi)

python graphviz graph citation deep-learning-papers google-scholar

Updated Dec 9, 2015
Python

mjpyeon / pytorch-infogan-oop

objected oriented implementation of InfoGAN using PyTorch

machine-learning deep-neural-networks deep-learning generative-adversarial-network infogan deep-learning-algorithms deeplearning deep-learning-papers deep-learning-tutorial infogan-pytorch

Updated Aug 5, 2018
Python

sairajk / Thumbnail-Generation

Fast and Accurate User constrained Thumbnail Generation using Adaptive Convolutions. | ICASSP 2019 [ORAL]

Updated Feb 2, 2019
Python

madrugado / deep-learning-nlp-rl-papers

Recent Deep Learning papers in NLU and RL

natural-language-processing reinforcement-learning deep-learning nlu deep-learning-papers

Updated Oct 5, 2019
Python

veritas9872 / Knowledge-Distillation-Task

My implementation of "Distilling the Knowledge in a Neural Network" on the CIFAR10 data set using Pytorch.

neural-network reproducibility deep-learning-papers knowledge-distillation teacher pytorch-implementation knowledge-distillation-task

Updated Feb 6, 2020
Python

CellEight / Pytorch-Adaptive-Instance-Normalization

A Pytorch implementation of the 2017 Huang et. al. paper "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization"

machine-learning computer-vision deep-learning paper pytorch style-transfer neural-networks datasets deep-learning-papers pretrained-weights huang

Updated Jul 13, 2020
Python

tdeboissiere / DeepLearningImplementations

Implementation of recent Deep Learning papers

deep-learning-papers

Updated Oct 23, 2020
Python

oke-aditya / pytorch_paper_implementations

Deep Learning Paper Implementations in PyTorch

deep-learning pytorch deep-learning-papers implementation-of-research-paper

Updated Nov 11, 2020
Python

sanderson4030 / SharpnessAwareMinimization

Sharpness Aware Minimization for Fastai

deep-learning deep-learning-papers fastai fastai-extensions sharpness-aware-minimization

Updated Feb 26, 2021
Python

dcmocanu / sparse-evolutionary-artificial-neural-networks

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

deep-neural-networks sparsity deep-learning scalability randomization neuroevolution deep-learning-algorithms classification evolutionary-algorithms artificial-neural-networks restricted-boltzmann-machine complex-networks deep-learning-papers multi-layer-perceptron generative-models sparse-neural-networks scalable-deep-learning sparse-training sparse-evolutionary-training adaptive-sparse-connectivity

Updated Jul 21, 2021
Python

HasibAlMuzdadid / Deep-Learning-Papers-Reading-Roadmap

Deep Learning papers reading roadmap for anyone who is eager to learn this amazing tech!

deep-learning deep-learning-algorithms deep-learning-papers deep-learning-architectures deep-learning-models

Updated May 8, 2022
Python

Kaleidophon / awesome-experimental-standards-deep-learning

Repository collecting resources and best practices to improve experimental rigour in deep learning research.

machine-learning deep-neural-networks research deep-learning resources awesome-list deep-learning-papers deep-learning-tutorial experimental-design deep-learning-research

Updated Mar 30, 2023
Python

pouyaka / tube_segmentation

tubular structure segmentation in histopathological images

medical-imaging image-segmentation deep-learning-papers digital-pathology pytorch-implementation tubular-objects

Updated Apr 26, 2023
Python

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