1. |
Prompt-Tuning: 深度解读一种新的微调范式 |
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2. |
面向开发者的 ChatGPT 提示工程 |
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3. |
IAIFI Summer School & Workshop |
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4. |
Git: submodule 子模块简明教程 |
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5. |
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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6. |
解决 GitHub 的 host 域名被限制的问题 |
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7. |
[Paper Summary] Complete Parameter Inference for GW150914 Using Deep Learning |
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8. |
Particle Swarm Optimization From Scratch Using Python |
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9. |
Bayes Inference, Bayes Factor, Model Selection |
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10. |
谱分析 (spectral analysis) 的 SciPy 代码解析 |
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11. |
Python 中负数取余问题 |
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12. |
恒 Q 变换 (Constant-Q transform) |
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13. |
Unit 3: Structure & Paragraphs(学术写作) |
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14. |
Python 装饰器之 Property: Setter 和 Getter |
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15. |
Unit 2: Verbs(学术写作) |
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16. |
Unit 1: Introduction; principles of effective writing(学术写作) |
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17. |
S 变换 (Stockwel transform) |
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18. |
Interactive GW simulation in JavaScript for NSs or BBHs |
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19. |
Linux/Unix 中 Screen 命令详解 |
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20. |
贝叶斯深度学习前沿进展 (朱军教授) |
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21. |
深度学习: 从理论到算法 (王力威教授) |
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22. |
累积引力波事件率图的 python 实现 |
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23. |
Markdown Elements for Hugo/Wowchemy |
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24. |
傅里叶变换算法及其 python 实现 |
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25. |
Docker 简易入门教程 |
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26. |
Ray Tutorial |
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27. |
关于感受野 (Receptive field) 你该知道的事 |
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28. |
Centos7/CUDA-9.2/cuDNN-7.3/MXNet-cu92/Floydhub 深度学习环境配置手册 |
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29. |
CS231n课程资料:循环神经网络惊人的有效性 |
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30. |
CS231n课程讲义翻译:神经网络3 |
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31. |
CS231n课程讲义翻译:神经网络2 |
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32. |
CS231n课程讲义翻译:神经网络1 |
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33. |
CS231n课程讲义翻译:卷积神经网络 |
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34. |
CS231n课程讲义翻译:反向传播 |
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35. |
CS231n课程讲义翻译:最优化 |
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36. |
CS231n课程讲义翻译:线性分类 |
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37. |
CS231n课程讲义翻译:图像分类 |
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38. |
Guest Lecture. Adversarial Examples and Adversarial Training |
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39. |
Guest Lecture. Efficient Methods and Hardware for Deep Learning |
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40. |
Lecture 14. Deep Reinforcement Learning |
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41. |
Lecture 13. Visualizing and Understanding |
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42. |
Lecture 12. Generative Models |
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43. |
Lecture 11. Detection and Segmentation |
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44. |
Lecture 10. Recurrent Neural Networks |
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45. |
Lecture 9. CNN Architectures |
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46. |
Lecture 8. Deep Learning Hardware and Software |
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47. |
Lecture 7. Training Neural Networks, part 2 |
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48. |
Lecture 6. Training Neural Networks, part I |
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49. |
Lecture 5. Convolutional Neural Networks |
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50. |
Lecture 4. Introduction to Neural Networks |
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51. |
Lecture 3. Loss Functions and Optimization |
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52. |
Lecture 2. Image Classification & K-nearest neighbor |
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53. |
Lecture 1. Computer vision overview & Historical context |
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54. |
S_Dbw 聚类评估指标(代码全解析) |
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55. |
数据科学入门之我谈 (2018) |
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56. |
$LaTeX$ 常用的数学符号收集与字体整理 |
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57. |
一段关于神经网络的故事 |
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58. |
为啥一定用残差图检查你的回归分析? |
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59. |
https://realpython.com/p |
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60. |
https://blog.csdn.net/mi |
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61. |
The Multivariate normal |
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62. |
ICA |
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63. |
https://docs.scipy.org/d |
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64. |
https://pythonhosted.org |
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65. |
A list of awesome resour |
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66. |
https://baijiahao.baidu. |
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67. |
ChatGPT
ChatGPT 中文指南: ht |
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68. |
浅析 Hinton 最近提出的 Capsule |
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69. |
https://www.cnblogs.com/ |
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70. |
singularity容器使用心得 https: |
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71. |
https://makeabilitylab.g |
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72. |
http://rlchina.org/
‘Uns |
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73. |
A guide for using the Wa |
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74. |
http://www.phys.ufl.edu/ |
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75. |
https://uvadlc-notebooks |
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76. |
https://arxiv.org/pdf/19 |
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77. |
Mahalanobis Distance – U |
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78. |
1906.02691 |
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79. |
2022.0112 GW next - Hema |
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80. |
Overview and Installatio |
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81. |
All Statistical tests:
h |
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82. |
Using machine learning t |
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83. |
'''
Buffer funct |
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84. |
https://wiseodd.github.i |
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85. |
Python里精确地四舍五入,以及你为什么需要少 |
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86. |
PESummary is a python pa |
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87. |
https://realpython.com/p |
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88. |
Testing the no-hair theo |
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89. |
For linux
https://blog.c |
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90. |
For a complete list of a |
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91. |
https://baike.baidu.com/ |
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92. |
https://textbooks.math.g |
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93. |
Kalman filtering
深度解读:卡尔 |
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94. |
How about that Bayes: Ba |
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95. |
改写为 python3,并且写成一个新的全新的 |
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96. |
Hierarchical Bayesian mo |
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97. |
Python numpy.hanning() 使 |
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98. |
A Conceptual Introductio |
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99. |
ICA |
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