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🏦 银行笔试面试经验分享及资料分享(help you pass the bank interview, and get a amazing bank offer!)
Natural Language Processing Tutorial for Deep Learning Researchers
Simultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
zerodohero / hover_net
Forked from vqdang/hover_netSimultaneous Nuclear Instance Segmentation and Classification in H&E Histology Images.
Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
This repo contains the code used for NeurIPS 2019 paper "Asymmetric Valleys: Beyond Sharp and Flat Local Minima".
zerodohero / senet.pytorch
Forked from moskomule/senet.pytorchPyTorch implementation of SENet
Distributed Asynchronous Hyperparameter Optimization in Python
PyTorch Tutorial for Deep Learning Researchers
Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
Popular ECG QRS detectors written in python
Learning to Learn using One-Shot Learning, MAML, Reptile, Meta-SGD and more with Tensorflow
Application of deep learning and convolutional networks for ECG classification
Application of deep learning and convolutional networks for ECG classification
Build your neural network easy and fast, 莫烦Python中文教学
Slice-based online convolutional dictionary learning.
Putting TensorFlow back in PyTorch, back in TensorFlow (differentiable TensorFlow PyTorch adapters).
【干货】史上最全的PyTorch学习资源汇总
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
A collection of various deep learning architectures, models, and tips
A collection of various deep learning architectures, models, and tips
Everything about Transfer Learning and Domain Adaptation--迁移学习
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Demo for Multi-Layer ISTA and Multi-Layer FISTA algorithms for convolutional neural networks, as described in J. Sulam, A. Aberdam, A. Beck, M. Elad, (2018). On Multi-Layer Basis Pursuit, Efficient...
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network