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Deep Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/PseudoCodeNerd/ml-road

Notes on Deep Learning for NLP

https://github.com/QTIM-Lab/DeepNeuro

DeepNeuro: an open-source deep learning toolbox for neuroimaging

https://github.com/QTIM-Lab/DeepRad

DeepNeuro: an open-source deep learning toolbox for neuroimaging

https://github.com/QUVA-Lab/e2cnn

Steerable Partial Differential Operators for Equivariant Neural Networks

https://github.com/QuarantineGemmini/chipyard

Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration

https://github.com/Ranja-S/sensitivity

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

https://github.com/Ranlot/backpropagation-CNNs

Deep learning for pedestrians: backpropagation in CNNs

https://github.com/RasmusRPaulsen/Deep-MVLM

Multi-view consensus CNN for 3D facial landmark placement

https://github.com/Rayryu/CNN-based-sentimental-analysis-multiclass-classification

Deep Learning applied to NLP

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

Deep Learning Face Representation by Joint Identification-Verification

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Deep Learning meets Liveness Detection: Recent Advancements and Challenges

https://github.com/ReubenDo/U-HVED

NiftyNet: a deep-learning platform for medical imaging

https://github.com/RezaCDoobary/DrugDiscovery-Tox21

Toxicity Prediction using Deep Learning

https://github.com/Riashat/Active-Learning-Bayesian-Convolutional-Neural-Networks

Deep Bayesian Active Learning with Image Data

https://github.com/Roberto09/Dynamic-Attention-Model-for-VRP---Pytorch

Combining Constructive and Perturbative Deep Learning Algorithms for the Capacitated Vehicle Routing Problem

https://github.com/Rohit102497/Aux-Drop

Online Deep Learning: Learning Deep Neural Networks on the Fly

https://github.com/Rose-STL-Lab/Turbulent-Flow-Net

Towards Physics-informed Deep Learning for Turbulent Flow Prediction

https://github.com/Rowing0914/Graph_Nets

Relational inductive biases, deep learning, and graph networks

https://github.com/Rui1521/AutoODE-DSL

Bridging Physics-based and Data-driven modeling for Learning Dynamical Systems

https://github.com/Ruoyiran/DeepID

Deep Learning Face Representation by Joint Identification-Verification

https://github.com/SANCHES-Pedro/Segmentation_3D_DeepLearning

Cerebrovascular Network Segmentation on MRA Images with Deep Learning

https://github.com/SAP-samples/machine-learning-diff-private-federated-learning

Deep Learning with Differential Privacy

https://github.com/SAP/machine-learning-diff-private-federated-learning

Deep Learning with Differential Privacy

https://github.com/SCXsunchenxi/ISMTS-Review

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

https://github.com/SFFAI-AIKT/AIKT-Natural_Language_Processing

A Survey of Deep Learning Techniques for Neural Machine Translation

https://github.com/STASVLAD/Tips-and-Tricks-in-DL

Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars

https://github.com/SachaIZADI/Misc-Machine-Learning

Applying Deep Learning To Airbnb Search

https://github.com/SafiyaJan/Attacking-Neural-Networks

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/Samarth-Tripathi/IEMOCAP-Emotion-Detection

Multi-Modal Emotion recognition on IEMOCAP Dataset using Deep Learning

https://github.com/SamuelGuilluy/Bayesian_ML_SWAG

A Simple Baseline for Bayesian Uncertainty in Deep Learning

https://github.com/SanaAwan5/transfer_learning_on_mnist_spdz

A generic framework for privacy preserving deep learning

https://github.com/Sang-Yeong/Dacon-Arctic_Ocean_Ice_Prediction

Numerical Weather Forecasting using Convolutional-LSTM with Attention and Context Matcher Mechanisms

https://github.com/SayedMaheen/60DaysOfUdacity

Deep Learning with Differential Privacy

https://github.com/ScorpLee/cnn-exmplainer-covid19

CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization

https://github.com/SeanPLeary/dc_tts-transfer-learning

Exploring Transfer Learning for Low Resource Emotional TTS

https://github.com/Sehaba95/Bark-Idenfication-using-BarkNet-1.0

Tree Species Identification from Bark Images Using Convolutional Neural Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.