RN-08516-001_v23.07 | July 2023 PyTorch Release Notes PyTorch RN-08516-001_v23.07 | ii Table of Contents Chapter 1. PyTorch Overview......................................................... 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 Conda package manager was installed in /opt/conda. NVIDIA PyTorch Container Versions The following table shows what versions of Ubuntu, CUDA, PyTorch, and TensorRT are supported in each of the NVIDIA containers

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 . . . . . . . . . . . . . . . . . . . . . . .

compressed sizes of our regular model and its 50% sparse version. We used Tensorflow's save_model() API and zipped the model files using gzip. In addition to the usual models, the figure also shows compressed elements is . Assuming , and as usual, the compression ratio using the above formula computes to: . Table 5-1 lists the compression ratios for different values of , using the above values of and . Number between the original tensor and the decoded tensor, as we did when working with quantization. 128 4.0 Table 5-1: Number of centroids v/s the compression ratio, assuming , and . As we increase the value of

please notify us at contact@tutorialspoint.com Keras iii Table of Contents About the Tutorial ................................................................ ........................................................................................... ii Table of Contents ..................................................................................... ..................................................... 55 Keras v Functional API ..................................................................................................

aggregate, this would be better than encoding each symbol with the same number of bits. The lookup table (figure 2-1 middle) that contains the symbol-code mapping is transmitted along with the encoded data Encoding & Huffman Tree. Source When decoding the encoded data, we look up the code from the lookup table to retrieve the symbols back. Since the codes are unique for each symbol (in fact, they are prefix we can easily construct the original sequence of symbols from the encoded sequence and the lookup table. Refer the wikipedia article on arithmetic coding to learn about lossless coding schemes. The lossy

vision for crowd sourced maps Hudl • Predictive analytics on sports plays Upserve • Restaurant table mgmt & POS for forecasting customer traffic TuSimple • Computer Vision for Autonomous Driving Clarifai 对话引擎 Rekognition 图像分析 深度学习框架 MXNet, TensorFlow, Theano, Caffe, Torch 为客户模型定制的 深度学习框架 人工智能 的托管的 API服务 Amazon AI: 新的深度学习服务 Polly Lex Rekognition 深度学习框架 MXNet, TensorFlow, Theano, Caffe, Torch 控制力 • 简单 • 混合了声明式（declarative）和命令式()代码的特点 为什么选择 MXNet ？ MXNet: 可扩展的深度学习框架 MXNet 框架的特点 命令式 NDArray API 声明式 Symbolic Executor MXNet： 博采众家之长 3D Image Construction https://github.com/piiswrong/deep3d

2017 年开始， Keras 的大部分组件被整合到 TensorFlow 框架中。2019 年，在 TensorFlow 2 版本中，Keras 被正式确定为 TensorFlow 的高层唯一接口 API，取代了 TensorFlow 1 版本中自带的 tf.layers 等高层接口。也就是说，现在只能使用 Keras 的接口来完成 TensorFlow 层方式的 模型搭建与训练。在 TensorFlow TensorFlow 中，Keras 被实现在 tf.keras 子模块中。 Keras 与 tf.keras 有什么区别与联系呢？其实 Keras 可以理解为一套搭建与训练神经网 络的高层 API 协议，Keras 本身已经实现了此协议，安装标准的 Keras 库就可以方便地调用 TensorFlow、CNTK 等后端完成加速计算；在 TensorFlow 中，也实现了一套 Keras 协议， 即 tf 息，不需要提前 创建模型即可直接从文件中恢复出网络 network 对象。 8.3.3 SavedModel 方式 TensorFlow 之所以能够被业界青睐，除了优秀的神经网络层 API 支持之外，还得益于 它强大的生态系统，包括移动端和网页端等的支持。当需要将模型部署到其他平台时，采 用 TensorFlow 提出的 SavedModel 方式更具有平台无关性。 通过 tf

视觉感知模型 SACC2017 视觉感知核心问题 Object Segmentation Object Classification Person, Horse, Barrier, Table, etc Object Detection 检测 识别 分割 跟踪 核 心 SACC2017 图像技术的三个核心难点>>小、快、准 小模型 线上速度快 预测准 Frequent remote Predictor 检测 RNN SACC2017 360小水滴摄像机：视觉大不同 小水滴·360智能摄像机 视觉大不同 你不在家时有它在 通过语音人工智能实现求救与留言功能 Cloud-API 每天调用1.5亿次!2000QPS! SACC2017 系统框架 n 根据业务需求，对图像人脸进行识别，将结果推送到业务端 n 基于深度学习的准确的人脸检测、特征抽取 n 人脸检测占用95%计算资源

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 leads to a direct increase in model size and memory consumption. Figure 1-16: A regular embedding table on the left with an embedding for each token. Hashing Trick on the right, where multiple tokens map

animals. The higher the value, the more that particular feature represents the given animal. In Table 4-1 we manually assigned values for the cute and dangerous features for six animals2, and we are calling cat (0.95, 0.05) snake (0.01, 0.9) bear (0.5, 0.95) raccoon (0.5, 0.5) mouse (0.01, 0.2) Table 4-1: A table consisting of embeddings of the various animals, using two features (cute and dangerous), each take a value between 0.0 and 1.0. We manually picked these values for illustration. Going through table 4-1, cat and dog have high values for the ‘cute’ feature, and low values for the ‘dangerous’ feature