would have needed if we were training a task specific model from scratch. One such task is the Microsoft Research Paraphrase Corpus1 where the model needs to predict if a pair of sentences are semantically software9 where GPT-3 is used for auto-completing code snippets with an IDE. End-users can also use GPT-3 API10 to build their own applications. Given the large number of possible uses for such models, the high Anthology, Nov. 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

..................................................... 55 Keras v Functional API .................................................................................................. and the primary benefit is distributed computing. CNTK is deep learning framework developed by Microsoft. It uses libraries such as Python, C#, C++ or standalone machine learning toolkits. Theano and techniques to make high level neural network API easier and more performant. It supports the following features:  Consistent, simple and extensible API.  Minimal structure - easy to achieve the

PyTorch Computation Graph Advantages (continued) • Which one do you think is better? Advantages (continued) • Which one do you think is better? PyTorch! • Easy Interface − easy to use API. The code execution • Visualization Tools like • TensorboardX (monitor training) • PyTorchViz (visualise computation graph) • Various other functions • loss (MSE,CE etc..) • optimizers Prepare Input Data •Load data •Iterate to run on. Visualization • TensorboardX (visualise training) • PyTorchViz (visualise computation graph) https://github.com/lanpa/tensorboardX/ Visualization (continued) • PyTorchViz https://github

3 提交主要更改 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764 16.6 d2l API 文档 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767 16.6 些情况下，我们通常会提供两个版本的示例：一个是我们从零开始实现一切，仅依赖张量操作和自动微分； 另一个是更实际的示例，我们使用深度学习框架的高级API编写简洁的代码。一旦我们教了您一些组件是如 何工作的，我们就可以在随后的教程中使用高级API了。 内容和结构 全书大致可分为三个部分，在 图1 中用不同的颜色呈现： 目录 3 图1: 全书结构 • 第一部分包括基础知识和预备知识。1节 的流行 工具，而MNIST数据集的60000个手写数字的数据集被认为是巨大的。考虑到数据和计算的稀缺性，核方法 （kernel method）、决策树（decision tree）和图模型（graph models）等强大的统计工具（在经验上）证明 是更为优越的。与神经网络不同的是，这些算法不需要数周的训练，而且有很强的理论依据，可以提供可预 测的结果。 1.5 深度学习的发展 大约2

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 Optimization: Active learning Data Label Model 证件扫描 活体检测 人脸比对 • 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 [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]

LSTM 模型 . . . . . . . . . . . . 15 3.2 函数式 API 指引 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.2.1 开始使用 Keras 函数式 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.2 Sequential 顺序模型 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.1 Sequential 顺序模型 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.3 函数式 API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 4.3.1 Model 类 API . . . . . . . . . . . . . . . . . . . . . . .

multi-gpu, and multi-node support. Functions are executed immediately instead of enqueued in a static graph, improving ease of use and provides a sophisticated debugging experience. In the container, see /workspace/README highly optimized modules for popular Transformer 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 highly optimized modules for popular Transformer 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

production. BERT is used in Google Search to improve relevance of results, and GPT-3 is available as an API for interested users to consume. Having demonstrated the rapid growth of deep learning models, let Figure 1-9: Illustration of the pruning process. On the left is the unpruned graph, and on the right is a pruned graph with the unimportant connections and neurons removed. Learning Techniques Learning

Auto Evaluation Log Server Graph Abstraction Data Flow API Manager Pipeline AVA 弹性深度学习平 台 L1 L2 L3 L4 L5 原子API 基础模型 感知层1 API 感知层2 API Vision 综合API 业务逻辑API Argus机器视觉系统 可自定义开发 Argus现有系统提供

quality image. b=2 b=4 b=5 b=6 b=7 b=8 Figure 2-7: Images with various degrees of quantization. A graph of quantized representation bit size (b) and the resulting image sizes (in bits) is shown in figure model. Now, let’s get it ready for training. The get_compiled_model() function creates our model graph using the create_model() function. Then, it compiles the model by providing the necessary components Unoptimized Model We are all set to start training our model. Tensorflow provides a user-friendly API to train the model. All we need is to invoke the fit() method on the model object. It takes in the