| 2023-07-27 |
https://textbooks.math.g |
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| 2023-05-30 |
Prompt-Tuning: 深度解读一种新的微调范式 |
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| 2023-05-30 |
面向开发者的 ChatGPT 提示工程 |
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| 2023-05-30 |
A guide for using the Wa |
|
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| 2023-02-23 |
ChatGPT
ChatGPT 中文指南: ht |
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| 2022-12-15 |
IAIFI Summer School & Workshop |
|
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| 2022-12-15 |
https://baijiahao.baidu. |
|
|
| 2022-12-15 |
Overview and Installatio |
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| 2022-12-15 |
https://www.cnblogs.com/ |
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| 2022-12-15 |
PESummary is a python pa |
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| 2022-12-15 |
singularity容器使用心得 https: |
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| 2022-12-15 |
https://pythonhosted.org |
|
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| 2022-03-06 |
Mahalanobis Distance – U |
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| 2022-03-06 |
2022.0112 GW next - Hema |
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| 2022-03-06 |
http://rlchina.org/
‘Uns |
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|
| 2022-03-06 |
Using machine learning t |
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| 2022-03-06 |
https://makeabilitylab.g |
|
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| 2022-03-06 |
How about that Bayes: Ba |
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|
| 2022-02-13 |
http://www.phys.ufl.edu/ |
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| 2022-02-13 |
https://blog.csdn.net/mi |
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| 2022-02-13 |
https://docs.scipy.org/d |
|
|
| 2021-09-29 |
A Conceptual Introductio |
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| 2021-09-29 |
https://realpython.com/p |
|
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| 2021-08-28 |
Git: submodule 子模块简明教程 |
|
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| 2021-08-28 |
https://www.researchgate |
|
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| 2021-08-17 |
GitHub 不再支持密码验证，如何在 macOS 上实现 Token 登陆配置 |
|
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| 2021-08-07 |
All Statistical tests:
h |
|
|
| 2021-08-07 |
Hierarchical Bayesian mo |
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|
| 2021-08-07 |
https://uvadlc-notebooks |
|
|
| 2021-08-03 |
https://wiseodd.github.i |
|
|
| 2021-08-03 |
浅析 Hinton 最近提出的 Capsule |
|
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| 2021-06-07 |
https://baike.baidu.com/ |
|
|
| 2021-05-12 |
The Multivariate normal |
|
|
| 2021-05-12 |
1906.02691 |
|
|
| 2021-05-04 |
For a complete list of a |
|
|
| 2021-05-04 |
Testing the no-hair theo |
|
|
| 2021-04-28 |
解决 GitHub 的 host 域名被限制的问题 |
|
|
| 2021-04-10 |
[Paper Summary] Complete Parameter Inference for GW150914 Using Deep Learning |
|
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| 2021-04-09 |
[Paper Summary] Complete parameter inference for GW150914 using deep learning |
|
|
| 2021-04-06 |
A list of awesome resour |
|
|
| 2021-04-04 |
Particle Swarm Optimization From Scratch Using Python |
|
|
| 2021-04-04 |
Bayes Inference, Bayes Factor, Model Selection |
|
|
| 2021-04-04 |
谱分析 (spectral analysis) 的 SciPy 代码解析 |
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| 2021-04-04 |
Python 中负数取余问题 |
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| 2021-04-04 |
恒 Q 变换 (Constant-Q transform) |
|
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| 2021-04-04 |
Unit 3: Structure & Paragraphs（学术写作） |
|
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| 2021-04-04 |
Python 装饰器之 Property: Setter 和 Getter |
|
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| 2021-04-04 |
Unit 2: Verbs（学术写作） |
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| 2021-04-04 |
Unit 1: Introduction; principles of effective writing（学术写作） |
|
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| 2021-04-04 |
S 变换 (Stockwel transform) |
|
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| 2021-04-04 |
Interactive GW simulation in JavaScript for NSs or BBHs |
|
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| 2021-04-04 |
Linux/Unix 中 Screen 命令详解 |
|
|
| 2021-04-04 |
贝叶斯深度学习前沿进展 (朱军教授) |
|
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| 2021-04-04 |
深度学习: 从理论到算法 (王力威教授) |
|
|
| 2021-04-04 |
累积引力波事件率图的 python 实现 |
|
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| 2021-04-04 |
Markdown Elements for Hugo/Wowchemy |
|
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| 2021-04-04 |
傅里叶变换算法及其 python 实现 |
|
|
| 2021-04-04 |
Docker 简易入门教程 |
|
|
| 2021-04-04 |
Ray Tutorial |
|
|
| 2021-04-04 |
关于感受野 (Receptive field) 你该知道的事 |
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| 2021-04-04 |
Centos7/CUDA-9.2/cuDNN-7.3/MXNet-cu92/Floydhub 深度学习环境配置手册 |
|
|
| 2021-04-04 |
CS231n课程资料：循环神经网络惊人的有效性 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：神经网络3 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：神经网络2 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：神经网络1 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：卷积神经网络 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：反向传播 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：最优化 |
|
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| 2021-04-04 |
CS231n课程讲义翻译：线性分类 |
|
|
| 2021-04-04 |
CS231n课程讲义翻译：图像分类 |
|
|
| 2021-04-04 |
Guest Lecture. Adversarial Examples and Adversarial Training |
|
|
| 2021-04-04 |
Guest Lecture. Efficient Methods and Hardware for Deep Learning |
|
|
| 2021-04-04 |
Lecture 14. Deep Reinforcement Learning |
|
|
| 2021-04-04 |
Lecture 13. Visualizing and Understanding |
|
|
| 2021-04-04 |
Lecture 12. Generative Models |
|
|
| 2021-04-04 |
Lecture 11. Detection and Segmentation |
|
|
| 2021-04-04 |
Lecture 10. Recurrent Neural Networks |
|
|
| 2021-04-04 |
Lecture 9. CNN Architectures |
|
|
| 2021-04-04 |
Lecture 8. Deep Learning Hardware and Software |
|
|
| 2021-04-04 |
Lecture 7. Training Neural Networks, part 2 |
|
|
| 2021-04-04 |
Lecture 6. Training Neural Networks, part I |
|
|
| 2021-04-04 |
Lecture 5. Convolutional Neural Networks |
|
|
| 2021-04-04 |
Lecture 4. Introduction to Neural Networks |
|
|
| 2021-04-04 |
Lecture 3. Loss Functions and Optimization |
|
|
| 2021-04-04 |
Lecture 2. Image Classification & K-nearest neighbor |
|
|
| 2021-04-04 |
Lecture 1. Computer vision overview & Historical context |
|
|
| 2021-04-04 |
S_Dbw 聚类评估指标（代码全解析） |
|
|
| 2021-04-04 |
数据科学入门之我谈 (2018) |
|
|
| 2021-04-04 |
一段关于神经网络的故事 |
|
|
| 2021-04-04 |
为啥一定用残差图检查你的回归分析？ |
|
|
| 2021-04-04 |
Python numpy.hanning() 使 |
|
|
| 2021-04-04 |
改写为 python3，并且写成一个新的全新的 |
|
|
| 2021-04-04 |
Kalman filtering
深度解读：卡尔 |
|
|
| 2021-04-04 |
For linux
https://blog.c |
|
|
| 2021-04-04 |
https://realpython.com/p |
|
|
| 2021-04-04 |
Python里精确地四舍五入，以及你为什么需要少 |
|
|
| 2021-04-04 |
'''
Buffer function li |
|
|
| 2021-04-04 |
https://arxiv.org/pdf/19 |
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