模板 | 如何画出漂亮的深度学习模型图

偏执的太偏执、 2022-08-28 03:49 746阅读 0赞

现在搞AI研究写论文，其中论文里的框架图模型图很是考验你画图的能力，不费一番心思功夫，怎能画出一个入得Reviewer法眼的图，论文也不大好中，很是发愁。好消息来了，**elvis介绍了有个ML visual的利器，提供一份32页的PPT模型图素材，你在上面可以直接使用画出你要的机器学习深度学习模型图，再也不用担心画图了！**

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比如要画一个 基于一个Transformer的模型的图，直接在上面修改就可以了，是不是很容易上手？

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**ML Visuals是一个新的协作项目**，通过使用更专业、更吸引人、更充分的图块来帮助机器学习社区改进科学传播。你可以在你的演讲或博客文章中自由使用视觉效果。使用任何视觉效果都不需要经过许可，但是如果你能提供设计师/作者(作者信息可以在幻灯片注释中找到)，那就太好了。

**怎么使用ML Visual？**

**下载这份MLvisual PPT，在上面的模板编辑你要的就行。**

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请加微信后备注：PPT模板

**机器学习深度学习模型素材32页PPT**

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请加微信后备注：PPT模板

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我是[王博Kings][Kings]，985AI博士，华为云专家、CSDN博客专家（人工智能领域优质作者）。单个AI开源项目现在已经获得了2100+标星。现在在做AI相关内容，欢迎一起交流学习、生活各方面的问题，一起加油进步！

我们微信交流群涵盖以下方向（但并不局限于以下内容）：人工智能，计算机视觉，自然语言处理，目标检测，语义分割，自动驾驶，GAN，强化学习，SLAM，人脸检测，最新算法，最新论文，OpenCV，TensorFlow，PyTorch，开源框架，学习方法...

这是我的私人微信，位置有限，一起进步！

**手推笔记：**

[思维导图][Link 1] | [“模型评估与选择”][Link 2][“线性模型”][Link 3][“决策树” | ][Link 4][“神经网络”][Link 5][支持向量机（上）][Link 6][支持向量机（下）][Link 6][贝叶斯分类（上）][Link 7][贝叶斯分类（下）][Link 8][集成学习（上）][Link 9][集成学习（下）][Link 10][聚类 | ][Link 11][降维与度量学习][Link 12][稀疏学习 | ][Link 13][计算学习理论][Link 14][半监督学习 | ][Link 15][概率图模型 ][Link 16]| [规则学习][Link 17]

**增长见识：**

[博士毕业去高校难度大吗？][Link 18] | [研读论文有哪些经验之谈？][Link 19][聊聊跳槽这件事儿][Link 20][聊聊互联网工资收入的组成][Link 21][机器学习硕士、博士如何自救？][Link 22][聊聊Top2计算机博士2021年就业选择][Top2_2021][非科班出身怎么][Link 23] [有哪些相见恨晚的][Link 24][经验 | 计算机专业科班出身如何提高自己编程能力？][Link 25][博士如何高效率阅读文献][Link 26] | [有哪些越早知道越好的人生经验？][Link 27] |

**其他学习笔记：**

[PyTorch张量Tensor][PyTorch_Tensor] | [卷积神经网络CNN的架构][CNN][深度学习语义分割][Link 28][深入理解Transformer][Transformer][Scaled-YOLOv4！][Scaled-YOLOv4][PyTorch安装及入门][PyTorch][PyTorch神经网络箱][PyTorch 1][Numpy基础][Numpy] | [10篇图像分类][10] | [CVPR 2020目标检测][CVPR 2020] | [神经网络的可视化解释][Link 29] | [YOLOv4全文解读与翻译总结][YOLOv4] |

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[Kings]: https://blog.csdn.net/Sophia_11/article/details/114274846
[Link 1]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247486763&idx=1&sn=da878ccafba79932be8eeca955697477&chksm=fcb71194cbc0988259f32b0b7a77fe7830c477c9c9a4fcb00f2e366c97d776574c2bc949e993&scene=21#wechat_redirect
[Link 2]: https://blog.csdn.net/qq_28869927/article/details/85115333
[Link 3]: https://blog.csdn.net/u010358304/article/details/81257595
[Link 4]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247486809&idx=2&sn=1db1d3e29268334b78faf1b981030974&chksm=fcb711e6cbc098f0db247fdf9739a4e82aaada8231d5b3c6c79b1c19e79d81e3216a7e7476d3&scene=21#wechat_redirect
[Link 5]: https://blog.csdn.net/wc13197389627/article/details/94399085
[Link 6]: https://blog.csdn.net/ch18328071580/article/details/94168411
[Link 7]: https://blog.csdn.net/qq_40520596/article/details/104947872
[Link 8]: https://blog.csdn.net/weixin_42137700/article/details/89667683
[Link 9]: https://blog.csdn.net/yutc0202/article/details/114857068
[Link 10]: https://blog.csdn.net/u012706792/article/details/81033063
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[Link 12]: https://blog.csdn.net/wangqi1113/article/details/80444916
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[Link 14]: https://blog.csdn.net/limuyia/article/details/106916840
[Link 15]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247495199&idx=3&sn=4e56ec540c4fa222d9e57804b554ea70&chksm=fcb4f2a0cbc37bb60193b8a653016a0d5ea933f1b9c69e39d1b06861813c78fc7c574b527454&scene=21#wechat_redirect
[Link 16]: http://mp.weixin.qq.com/s?__biz=MzU2NTc5MjIwOA==&mid=2247503912&idx=1&sn=7cd6fee3a4fd042be5ae8f54d750052b&chksm=fcb4cc97cbc345818ec45020b51297150f8b2f1f211f8d74523529f02e14a47af812a5827443&scene=21#wechat_redirect
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[Link 18]: https://blog.csdn.net/weixin_42123296/article/details/119049634
[Link 19]: https://blog.csdn.net/zhangbochn/article/details/82250592
[Link 20]: https://blog.csdn.net/plokmju88/article/details/103354961
[Link 21]: https://blog.csdn.net/qq_15698613/article/details/116756749
[Link 22]: https://blog.csdn.net/qq_15698613/article/details/116756172
[Top2_2021]: https://blog.csdn.net/Sophia_11/article/details/112975439
[Link 23]: https://blog.csdn.net/weixin_43802541/article/details/116164322
[Link 24]: https://blog.csdn.net/data_cola/article/details/111285385
[Link 25]: https://blog.csdn.net/Sophia_11/article/details/113355807
[Link 26]: https://blog.csdn.net/woshicver/article/details/105304746
[Link 27]: https://blog.csdn.net/Sophia_11/article/details/103524731
[PyTorch_Tensor]: https://blog.csdn.net/winycg/article/details/100813519
[CNN]: https://blog.csdn.net/woniu201411/article/details/91987374
[Link 28]: https://blog.csdn.net/u013580397/article/details/78508392
[Transformer]: https://blog.csdn.net/zhaojc1995/article/details/109276945
[Scaled-YOLOv4]: https://blog.csdn.net/wangdongwei0/article/details/111621029
[PyTorch]: https://blog.csdn.net/liang_xiao_yun/article/details/79540254
[PyTorch 1]: https://blog.csdn.net/qq_39683307/article/details/80543965
[Numpy]: https://blog.csdn.net/dafeidouzi/article/details/100520906
[10]: https://blog.csdn.net/lvsehaiyang1993/article/details/80342473
[CVPR 2020]: https://blog.csdn.net/u013685264/article/details/104984809
[Link 29]: https://blog.csdn.net/fengdu78/article/details/107308594
[YOLOv4]: https://blog.csdn.net/fengdu78/article/details/106450074
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