/Miniconda3-latest-Linux-x86_64.sh • After Miniconda is installed: conda install pytorch -c pytorch Writing code • Up to you; feel free to use emacs, vim, PyCharm, etc. if you want. • Our recommendations: • Install: 1234:localhost:1234 __@__.cs.princeton.edu • First blank is username, second is hostname Jupyter Notebook VS Code • Install the Python extension. • ???????????? Install the Remote Development extension. • Python Jupyter notebooks by delimiting cells/sections with #%% • Debugging PyTorch code is just like debugging any other Python code: see Piazza @108 for info. Also try Jupyter Lab! Why talk about libraries

architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. ‣ A preview of Torch-TensorRT (1.4.0dev0) is now included. Torch-TRT is the TensorRT integration architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. PyTorch Release 23.06 PyTorch RN-08516-001_v23.07 | 15 ‣ A preview of Torch-TensorRT (1.4 architectures and an automatic mixed precision-like API that can be used seamlessly with your PyTorch code. ‣ NVIDIA Deep Learning Profiler (DLProf) v1.8, which was included in the 21.12 container, was the

required libraries to start with. We will use the gzip python module for demonstrating compression. The code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator same number of weights pruned. Phew! It feels like we have gone through a lot of talk without much code! In chapter four, we trained a model to predict masks for pets to build snapchat like filters. Let’s prunable block using magnitude-based pruning. Note that the below code is in addition to the original segmentation project in chapter four. The code for this project is available as a Jupyter notebook here.

Auto-Encoders 主讲：龙良曲 Outline Supervised Learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Massive Unlabeled data Unsupervised Learning https://medium.com/ to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5 Adversarial AutoEncoders ▪ Distribution of hidden code https://towardsdatascience.com/a-wizards-guide-to-adversarial-autoencoders-part-2- exploring-lat

Anaconda to my PATH environment variable”一项，这样可以通过命令行方式调用 Anaconda 程序。如图 1.23 所示，安装程序 询问是否连带安装 VS Code 软件，选择 Skip 即可。整个安装流程约持续 5 分钟，具体时间 预览版202112 第 1 章 人工智能绪论 18 需依据计算机性能而定。 图 1.22 Anaconda 语言编写程序的方式非常多，可以使用 ipython 或者 ipython notebook 方式 交互式编写代码，也可以利用 Sublime Text、PyCharm 和 VS Code 等综合 IDE 开发中大型 项目。本书推荐使用 PyCharm 编写和调试，使用 VS Code 交互式开发，这两者都可以免费 使用，用户自行下载安装，并配置好 Python 解释器即可。限于篇幅，这里不再赘述。 预览版202112 Henderson, R. E. Howard, W. Hubbard 和 L. D. Jackel, “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Comput., 卷 1, pp. 541-551, 12 1989. [3] A. Krizhevsky, I. Sutskever 和 G

without Collision 3 Marines (ours) vs. 1 Super Zergling (enemy) Hit and Run Tactics 3 Marines (ours) vs. 1 Zealot (enemy) Coordinated Cover Attack 3 Marines (ours) vs. 1 Super Zergling (enemy) Focus Focus Fire without Overkill 15 Marines (ours) vs. 16 Marines (enemy) Coordinated Heterogonous Agents 2 Dropships and 2 tanks vs. 1 Ultralisk Hierarchical Reinforcement Learning Strategy & Planning Combat

can be used to infer uncertainty. A one-vs-one SVM approach can be used to tackle multiple classes. Feng Li (SDU) Overview September 6, 2023 47 / 57 Parametric vs Non-Parametric Models Parametric model regression ˆy = ˆβ 0 + p � j=1 ˆβ jxj Feng Li (SDU) Overview September 6, 2023 48 / 57 Parametric vs Non-Parametric Models (Contd.) Non-parametric model: nearest-neighbor method Make predictions for that aren’t relevant to the test case. Feng Li (SDU) Overview September 6, 2023 49 / 57 Parametric vs Non-Parametric Models (Contd.) These two methods are opposite w.r.t. computation. NN-like methods

《TensorFlow 2 项目进阶实战》视频课程 基础理论篇：TensorFlow 2 设计思想 • TensorFlow 2 设计原则 • TensorFlow 2 核心模块 • TensorFlow 2 vs TensorFlow 1.x • TensorFlow 2 落地应用 目录 TensorFlow 2 设计原则 TensorFlow - Infra of AI TensorFlow 2 Support Limited Support Limited Support SavedModel：生产级 TensorFlow 模型格式 TensorFlow 2 vs TensorFlow 1.x Keras vs TensorFlow 1.x TensorFlow 1.x 工作流 Full of abstract notions TensorFlow 2 工作流 Native

Feng Li (SDU) Logistic Regression September 20, 2023 24 / 29 Transformation to Binary One-vs.-rest (one-vs.-all, OvA or OvR, one-against-all, OAA) strategy is to train a single classifier per class, classifier Feng Li (SDU) Logistic Regression September 20, 2023 25 / 29 Transformation to Binary One-vs.-One (OvO) reduction is to train K(K − 1)/2 binary classifiers For the (s, t)-th classifier: Positive

com/battle-of-the-deep-learning-frameworks-part-i-cff0e3841750 大浪淘沙 王者之争 https://www.edureka.co/blog/pytorch-vs-tensorflow/ 动态图 https://towardsdatascience.com/battle-of-the-deep-learning-frameworks-part-i-cff0e3841750 41750 静态图 综合评价 PyTorch TensorFlow 1 TensorFlow 2 性能 生态 工业界 学术界 上手难度 易用性 兼容性 发展前景 0 小结 VS PyTorch生态 TorchVision PyTorch能做什么？ • GPU加速 • 自动求导 • 常用网络层 1. GPU加速 2. 自动求导 3. 常用网络层 ▪ nn