widely-used deep learning frameworks such as PyTorch. PyTorch is a GPU-accelerated tensor computational framework with a Python front end. Functionality can be easily extended with common Python libraries such layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality. PyTorch also includes standard defined neural more information. The deep learning frameworks, the NGC Docker containers, and the deep learning framework containers are stored in the nvcr.io/nvidia repository. PyTorch RN-08516-001_v23.07 | 3 Chapter

Keras ii About the Tutorial Keras is an open source deep learning framework for python. It has been developed by an artificial intelligence researcher at Google named Francois the field of deep learning and neural network framework. This tutorial is intended to make you comfortable in getting started with the Keras framework concepts. Prerequisites Before proceeding concepts given in this tutorial, we assume that the readers have basic understanding of deep learning framework. In addition to this, it will be very helpful, if the readers have a sound knowledge of Python

无需分词：基于字符粒度表达的问答系统设计 L.X Meng, Y.Li, M.Y Liu, P Shu. Skipping Word: A Character-Sequential Representation based Framework for Question Answering. CIKM2016, pages 1869-1872, 2016. Sogou Inc 文本相关性计算 文本相关性计算 深度学习在搜狗搜索广告的一些应用 查询特征 广告特征 匹配特征 线性模型 非线性模型 Data Feature Model 线上Server CTR预估 Rank Online 特征抽取 CTR预估涉及技术 CTR预估 数据 模型 平台 MPI XgBoost Parameter Server 线性(LR) 非线性(GBDT) 深度(DNN) 实时(FTRL) 特征 训练数据 融合模型 Feature Maker One Case ALL One Hot 特征 Final CTR Bidding Server OFFLINE ONLINE OneHot Float LR Model DNN Model Retriever Server CTR Table DNN Model Feature LR Model Feature 特 征 池

内存成为主要资源瓶颈。由于需要等待全部参数 就绪，Parameter Server难以利⽤速度慢的存储 介质 样本读取 样本解析 参数拉 取 训练 参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 查询Sparse Table 查询Dense Tensor 参数更新 查询Sparse Table 查询Dense Tensor Reader Learner Worker 返回参数 Request Handler Parameter Server 更新参数 � 异步参数处理流⽔线 参数 预准备 Batch⼊队列 Batch⼊队列 � 效果： � 在不影响训练效果的情况下，降低参数准备与更新耗时，提 ⾼训练速度。训练耗时下降超50% 更基础的复杂模型，场景的快速适应 � 多场景建模 � 端云⼀体的协同 推荐技术 [KDD2020] DCAF: A Dynamic Computation Allocation Framework for Online Serving System � 推荐全链路⾃适应 � 统⼀建模，根据请求量削峰填⾕，资源利⽤最⼤化 [ijcai2021] UNBERT: User-News

Having such a toolbox to make our models pareto-optimal has the following benefits: Sustainable Server-Side Scaling Training and deploying large deep learning models is costly. While training is a one-time inference can be run completely on the user’s device without the need to send the input data to the server-side. New Applications Efficiency would also enable applications that couldn’t have otherwise been device. This further reduces the available resources for a single model. This could happen on the server-side where multiple models are co-located on the same machine, or could be in an app where different

conda install pytorch -c pytorch • Others via pip: pip3 install torch • ???????????? On Princeton CS server (ssh cycles.cs.princeton.edu) • Non-CS students can request a class account. • Miniconda is highly jupyter • ???????????? Run on your computer • jupyter notebook • ???????????? Run on Princeton CS server • Pick any 4-digit number, say 1234 • ???????????? hostname -s • ???????????? jupyter notebook --no-browser you think is better? PyTorch! • Easy Interface − easy to use API. The code execution in this framework is quite easy. Also need a fewer lines to code in comparison. • It is easy to debug and understand

标准化模型库  标准化解决方案 1.方案复杂 图像 搜索 推荐 语音 视频理解 NLP 广告 CNN RNN GNN MLP Tensorflow PyTorch Parameter Server MPI TreeModel SQL MapReduce Blink  场景丰富: 图像/视频/推荐/搜索  大数据+大模型: Model Zoo  跨场景+跨模态  开箱即用: 开箱即用: 封装复杂性  白盒化, 可扩展性强  积极对接开源系统+模型 FTRL SGD Adam Solutions Librarys 优势: Components Framework EasyVision EasyRec GraphLearn EasyTransfer 标准化: Standard Libraries and Solutions 标准化: Standard 卡证OCR • 人脸检测 • 活体检测 •人脸比对 Mobile SDK API + customer 示例: e-Know Your Customer eKYC eKYC Server eKYC SDK/API  多语言、国际化  多种证件版式  准确率领先同类产品  集成方便 标准化: Standard Solutions 智能推荐解决方案: 推荐请求 PAI-Studio–建模平台

dissimilar. How do we go about creating positive pairs? One example of such a recipe is the SimCLR framework12,13 (refer to Figure 6-10). SimCLR creates positive pairs by using different data augmentations enforce agreement between and . Figure 6-10: Contrastive learning as implemented in the SimCLR framework. The input is augmented to generate two views, and . Using the shared encoder , hidden 13 Chen Learners." arXiv, 17 June 2020, doi:10.48550/arXiv.2006.10029. 12 Chen, Ting, et al. "A Simple Framework for Contrastive Learning of Visual Representations." arXiv, 13 Feb. 2020, doi:10.48550/arXiv.2002

Apple’s CoreML as well which are covered in chapter 10. If you are not familiar with the tensorflow framework, we refer you to the book Deep Learning with Python1. All the code examples in this book are available to CPU, GPU, and TPU resources. You can also run this locally on your machine using the Jupyter framework or with other cloud services. The solution to this specific exercise is in this notebook. Solution: create_model() function. Then, it compiles the model by providing the necessary components the framework needs to train the model. This includes the loss function, the optimizer, and finally the metrics

com/star-history/ TensorFlow ������ https://timqian.com/star-history/ TensorFlow ��-TFX ML is more than a framework TFX - �� TensorFlow ���������� Baylor, Denis, et al. "Tfx: A tensorflow-based production-scale