Pinned Repositories
bigData
大数据比赛项目库
CNN_Course
从零开始实现一个卷积神经网络
Deep-Learning
:computer:深度学习实战:手写数字识别、Discuz验证码识别、垃圾分类、语义分割
deep-learning-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,近30万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
DL-FEM
深度学习的方法实现建模
Generative_Modeling_TMP
Improving-Generalization
LLMs_interview_notes
该仓库主要记录 大模型(LLMs) 算法工程师相关的面试题
MAGIC
Hushujin's Repositories
Hushujin/DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,近30万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Hushujin/bigData
大数据比赛项目库
Hushujin/CNN_Course
从零开始实现一个卷积神经网络
Hushujin/Deep-Learning
:computer:深度学习实战:手写数字识别、Discuz验证码识别、垃圾分类、语义分割
Hushujin/deep-learning-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
Hushujin/DL-FEM
深度学习的方法实现建模
Hushujin/Generative_Modeling_TMP
Hushujin/Improving-Generalization
Hushujin/LLMs_interview_notes
该仓库主要记录 大模型(LLMs) 算法工程师相关的面试题
Hushujin/MAGIC
Hushujin/Maryam-Toloubidokhti_2022_miccai
Hushujin/medical-imaging-datasets
A list of Medical imaging datasets.
Hushujin/PINNpapers
Must-read Papers on Physics-Informed Neural Networks.
Hushujin/python-100-
Hushujin/python-pytorch
Hushujin/RKN
Hushujin/SETR
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
Hushujin/structuredinference
Structured Inference Networks for Nonlinear State Space Models
Hushujin/tikhonov
code for L2 regularization of arbitrary Tikhonov matrices
Hushujin/Transformer
Transformer: PyTorch Implementation of "Attention Is All You Need"
Hushujin/u_net_liver
Hushujin/Unsupervised_EUSIPCO_22
Unsupervised Kalman Gain Estimation
Hushujin/Variational-Autoencoder-PyTorch
Variational Autoencoder implemented with PyTorch, Trained over CelebA Dataset