Problematic Files/Input Accuracy Modified from https://upload.wikimedia.org/wikipedia/commons/d/dc/UnderwoodKeyboard_%28transparent%29.png https://upload.wikimedia.org/wikipedia/commons/1/18/1328102022_Document 02022_Document.png May be re-distributed in accordance with the terms of the CC-SA 4.0 license https://creativecommons.org/licenses/by-sa/4.0/deed.en AAPL FB 700 GOOG TXT BIDU ? ? ? ? © 2018 Bloomberg from https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:Moving_From_unknown_to_known_feature_spaces_based_on_TS-ELM_with_random_kernels_and_connections.tif https://commons

Vijayvargeeya, Muhyun Kim, dennismalmgren, adursun, Anirudh Dagar, liqingnz, 3 http://learnpython.org/ 4 https://discuss.d2l.ai/ 6 目录 Pedro Larroy, lgov, ati‐ozgur, Jun Wu, Matthias Blume, Lin Yuan, geogunow 5 https://discuss.d2l.ai/ 目录 7 练习 1. 在本书discuss.d2l.ai6的论坛上注册帐户。 2. 在计算机上安装Python。 3. 沿着本节底部的链接进入论坛，在那里可以寻求帮助、讨论这本书，并通过与作者和社区接触来找到问 题的答案。 Discussions7 6 https://discuss.d2l.ai/ 7 https://discuss 接下来，初始化终端Shell，以便我们可以直接运行conda。 ~/miniconda3/bin/conda init 现在关闭并重新打开当前的shell。并使用下面的命令创建一个新的环境： 8 https://conda.io/en/latest/miniconda.html 9 conda create --name d2l python=3.9 -y 现在激活 d2l 环境： conda

深度学习：GAN 主讲人：龙良曲 https://blog.openai.com/generative-models/ Our Goal: ?(?) https://www.mathworks.com/help/stats/simulate-data-from-a-gaussian-mixture- model.html What does ? ? looks like? http://www Discriminator https://towardsdatascience.com/generative-adversarial-networks-explained- 34472718707a How to train? https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html Done! https://medium realistic samples https://drive.google.com/drive/folders/1lWC6XEPD0LT5KUnPXeve_kWeY-FxH002 Having Fun ▪ https://reiinakano.github.io/gan-playground/ ▪ https://affinelayer.com/pixsrv/ ▪ https://www.youtube

PAI平台的优势 1. 机器学习PAI: https://help.aliyun.com/product/30347.html 2. 阿里灵杰:https://www.zhihu.com/org/a-li-ling-jie 3. EasyRec: https://github.com/alibaba/EasyRec 4. 推荐解决方案: https://help.aliyun.com/document_detail/161927 com/document_detail/161927.html 5. EasyCV:https://github.com/alibaba/EasyCV 6. EasyNLP: https://github.com/alibaba/EasyNLP 7. AliGraph: https://github.com/alibaba/graph-learn 8. DSW: https://help.aliyun.com/document_detail/194831 com/document_detail/194831.html 9. DLC: https://help.aliyun.com/document_detail/165124.html 10. EAS: https://help.aliyun.com/document_detail/110980.html 11. Blade: https://help.aliyun.com/document_detail/205128

主讲：龙良曲 Outline Supervised Learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Massive Unlabeled data Unsupervised Learning https://medium.com/intuitionmachine/pre to process ▪ Visualization: https://projector.tensorflow.org/ ▪ Taking advantages of unsupervised data ▪ Compression, denoising, super-resolution … Auto-Encoders https://towardsdatascience.com/ap com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders

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ca/~vincentp/ift3395/lectures/backprop_old.pdf Multi-Output Perceptron ▪ Extend to cope with multi categories https://youtu.be/aygSMgK3BEM Perceptrons’ Limitation: 1969 http://science.sciencemag.org/content/165/3895/780 umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf 1986 https://web.stanford.edu/class/psych209a/ReadingsByDate/02_06/PDPVolIChapter8.pdf https://www.cs.cmu.edu/~epxing/Class/10715/reading/Kornick_et_al prop_old.pdf On DSP http://www.iro.umontreal.ca/~vincentp/ift3395/lectures/backprop_old.pdf ▪ https://youtu.be/FwFduRA_L6Q ▪ At some point in the late 1990s, one of these systems was reading 10 to

admin privileges required • It’s lightweight and fits within your disk quota • Instructions: • wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh • chmod u+x ./Miniconda3-latest-Linux-x86_64 load(PATH)) • model.eval() • CONVENTION IS TO SAVE MODELS USING EITHER A .PT OR A .PTH EXTENSION https://pytorch.org/tutorials/beginner/saving_loading_models.html Saving / Loading Weights (continued) (visualise training) • PyTorchViz (visualise computation graph) https://github.com/lanpa/tensorboardX/ Visualization (continued) • PyTorchViz https://github.com/szagoruyko/pytorchviz References • Important

generation and hence has been 8 BERT model on Tensorflow-Hub: https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4 7 GPU pricing source: https://cloud.google.com/compute/gpus-pricing. Numbers reported reported from October 2022. 6 Cloud TPU pricing source: https://cloud.google.com/tpu/pricing. Numbers reported from October 2022. extensively used for such tasks. Refer to figure 6-7 for an example where 2021, pp. 10644-52, doi:10.18653/v1/2021.emnlp-main.831. 10 OpenAI GPT-3 API https://openai.com/api/ 9 GitHub Copilot: https://github.com/features/copilot import tensorflow_datasets as tfds with tf.

功能。 核心图像库旨在快速访问以几种基本像素格式存储的数据， 它应该为一般的图像处理工 具提供坚实的基础。 https://github.com/python-pillow/Pillow captcha Catpcha 是一个生成图像和音频验证码的开源工具库。 https://github.com/lepture/captcha from captcha.image import ImageCaptcha GraphViz：将图形渲染为PDF，PNG，SVG等格式文件，需独立安装。 https://github.com/lepture/captcha flask flask 是一个基于 Werkzeug 和 jinja2 开发的 Python Web 应用程序框架，遵从 BSD 开源协 议。它以一种简约的方式实现了框架核心，又保留了扩展性。 https://github.com/pallets/flask 生成验证码数据集 Recognition）之类的计算机程 序自动识别出图片上的文数字而失去效果。由于这个测试是由计算机来考人类，而不是 标准图灵测试中那样由人类来考计算机，人们有时称CAPTCHA是一种反向图灵测试。 https://zh.wikipedia.org/wiki/captcha 验证码（CAPTCHA）破解 一些曾经或者正在使用中的验证码系统已被破解。 这包括Yahoo验证码的一个早期版本 EZ-Gi