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Natural Language Processing Tutorial for Deep Learning Researchers
Practice on cifar100(ResNet, DenseNet, VGG, GoogleNet, InceptionV3, InceptionV4, Inception-ResNetv2, Xception, Resnet In Resnet, ResNext,ShuffleNet, ShuffleNetv2, MobileNet, MobileNetv2, SqueezeNet…
Implementation of deep learning models for time series in PyTorch.
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
A tutorial demonstrating how to implement deep learning models for time series forecasting
Deep Learning for Time Series Classification
练习下用pytorch来复现下经典的推荐系统模型, 如MF, FM, DeepConn, MMOE, PLE, DeepFM, NFM, DCN, AFM, AutoInt, ONN, FiBiNET, DCN-v2, AFN, DCAP等
Pytorch Implementation of variational auto-encoder for MNIST
Pytorch implementation of Hyperspherical Variational Auto-Encoders
t2vec: Deep Representation Learning for Trajectory Similarity Computation
Code of CIKM'22 paper Jointly Contrastive Learning on Road Network and Trajectory
Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
TensorFlow implementation of TabTransformer
Implements of Awesome RecSystem Models with PyTorch/TF2.0
一些经典的CTR算法的复现; LR, FM, FFM, AFM, DeepFM, xDeepFM, PNN, DCN, DCNv2, DIFM, AutoInt, FiBiNet,AFN,ONN,DIN, DIEN ... （pytorch, tf2.0）
some ctr model, implemented by PyTorch, such as Factorization Machines, Field-aware Factorization Machines, DeepFM, xDeepFM, Deep Interest Network
Here are the models listed in CTR. Example: FM、DeepFM、xDeepFM etc.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.
PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
A simple and working implementation of Electra, the fastest way to pretrain language models from scratch, in Pytorch
A PyTorch-based implementation that leverages Transformer architectures to enhance the handling and design of tabular data. With the capabilities of Transformer models, we aim to provide data scien…
Research on Tabular Deep Learning: Papers & Packages