language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True 5-7B-Chat", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."} ], }' 或者您可以按照下面所示的方式，使用 openai Python model="Qwen/Qwen1.5-7B-Chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me something about large language models."}, ] ) print("Chat response:", chat_response)

implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan He, Lizi Liao, Hanwang Zhang implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan He, Lizi Liao, Hanwang Zhang implicit feedback. The training data for this model should contain binary information about whether a user interacted with a specific item. NCF was first described by Xiangnan He, Lizi Liao, PyTorch Release

千亿级特征（TB级）的模型的在线/离 线训练，在线推理服务和持续上线 O2. 针对推荐特点的深度优化，达到业界先 进⽔平 推荐系统的核⼼特点 � Feature 1（基本特点） 1.1 User与推荐系统交互，7*24⼩时 流式学习 1.2 Item和User新增，离开/遗忘， Embedding空间动态变化。 短期命中的⾼频key随时间缓慢变化 少量的⾼频key占据了主要访问需求 ⼀段时间样 本命中的 unique 推荐系统 模型上线 在线推理 模型训练 ⽂章 新闻 视频 Item User Item特征 ⽤户反馈 Item推荐 Embedding参数 本⼩时访问过的key 上⼩时访问过的key 访 问 百 分 ⽐ 时间（⼩ 时） � Feature 2（数据的时空特点） 2.1 短时间内只有部分item和user被 命中，只有部分参数被⽤到 � Feature 3（机器学习的特点） 训练框架—基于参数服务器架构的分布式训练框架 TB级模型 分⽚ 存储/更新 百TB数据 分⽚训练 Feature 1: 动态空间 Feature 2.1:短时间内只有部分item和user 被命中，只有部分参数被⽤到 参数按需 获取/更新 Storage 异步训练流⽔线和多级存储：提升性能，降低内存成本 � 问题： � Learner线程中参数拉取和参数更新对性能影响⼤

solving? For example, when a model is trained to predict if a given tweet contains offensive text, the user should be aware of how many GPUs / TPUs are needed and for how long to converge to a good accuracy inference latency, etc. Using the sensitive tweet classifier example, during the deployment phase the user will be concerned about the inference efficiency and should be aware of what is the inference latency on. Privacy & Data Sensitivity Being able to use as little data for training is critical when the user-data might be sensitive to handling / subject to various restrictions such as the General Data Protection

default shell initialization script set by cssystem is ~/.cshrc_user , so you should write the content in ~/.tcshrc to ~/.cshrc_user : source "/export/data/miniconda3/etc/profile.d/conda.csh" conda /rwproject/kdd-db/`whoami`/miniconda3/bin/conda init tcsh Since if ~/.tchsrc exists, ~/.cshrc_user won't be loaded, so you need to remove ~/.tcshrc : 4. Log out and log in again. If Miniconda is to use GPUs. The output should be True if PyTorch is able to use GPUs. cat ~/.tcshrc >> ~/.cshrc_user rm ~/.tcshrc conda --help conda create -n pytorch conda activate pytorch (pytorch) rwcpu8.cse

London Heathrow Seattle 02/24/2017 Hotel Booking 与 AWS Mobile Hub 集成 Authenticate users Analyze user behavior Store and share media Synchronize data More …. Track retention Conversational Bots Lex Amazon Lex Mobile App Mobile Hub SaaS Connector Amazon API Gateway AWS Lambda 1: Understand user intent Amazon API Gateway AWS Lambda 3: Translate REST response into natural language Mobile Invoke a SaaS application or an existing business application Business Application Firewall User Input 应用案例: Capital One “A highly scalable solution, it also offers potential to speed time to market

content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part achieve the result without any frills.  It supports multiple platforms and backends.  It is user friendly framework which runs on both CPU and GPU.  Highly scalability of computation. Benefits your installation location. Windows 2. Keras ― Installation Keras 4 Windows user can use the below command, py -m venv keras Step 2: Activate the environment This step will

• 优势：简单高效、可解释性强 • 局限性：特征工程繁琐、无法表达高维抽象特征 Ø 深度学习模型（DNN based model） • 优势： 泛化能力强 表达能力强 网络结构灵活 User features Relation features Contextual features Continueous featues Categorical features normalize sampling：依据微博的 平均阅读时间进行划分，将用户曝 光但未阅读的微博作为负样本 • 网络复杂度过高易导致过拟合 • 网络深度达到一定数值AUC反而 小幅降低 深度学习应用实践 —— DeepFM User features Relation features Contextual features Continueous featues Categorical features normalize

EasyVision: 图像视频算法库 Bert TextInput Optim izer 性能优越:  分布式存储  分布式查询 功能完备:  GSL/负采样  主流图算法  异构图 (user/item/attribute)  动态图 标准化: Standard Libraries Graph-Learn: 分布式图算法库 标准化: Standard Solutions Continuous [VariationalDropout] 通信优化 [GRPC++] 实时训练 [增量更新] 混合精度 [bf16] 工程优化: 千亿特征优化 模型蒸馏 AVX/SSE优化 Graph优化 [User Graph去重] 内存Allocate优化 ParallelStringOp [split/type conversion] Sequence Feature [side info] Op

视觉AI负责人，专家研究员 个人主页：http://xiaoyongshen.me/ Google Scholar： https://scholar.google.com/citations?user=P eMuphgAAAAJ&hl=en 看得更清，看得更懂 目录 1. 夜景增强 2. 图像视频去模糊 3. 视频超分辨率 1. 夜景图像增强 Taking photos is easy JieP HDRNet DPE White-box Distort-and-Recover Our result Expert-retouched More Comparison Results: User Study Input WVM JieP HDRNet DPE White-Box Distort-and-Recover Our result Limitaion Input Our result