业务落地篇：实现货架洞察 Web 应用 扫码试看/订阅 《 TensorFlow 2项目进阶实战》视频课程 • 串联 AI 流程理论：商品检测与商品识别 • 串联 AI 流程实战：商品检测与商品识别 • 展现 AI 效果理论：使用 OpenCV 可视化识别结果 • 展现 AI 效果实战：使用 OpenCV 可视化识别结果 • 搭建 AI SaaS 理论：Web 框架选型 • 搭建 AI 展现 AI 效果实战：使用 OpenCV 可视化识别结果 “Hello TensorFlow” Try it！ 搭建 AI SaaS 理论：Web 框架选型 Python Web 框架 Python Web 框架 - Flask Python Web 框架 - Flask Flask 常用扩展 Flask 项目常见目录结构 启动文件 manage.py 示例 搭建 AI SaaS 理论：数据库 --rm --name tf2_ai_saas -p 9000:9000 tf2-ai-saas bash 使用 cURL 发起识别请求 $ curl -H "Content-Type: application/json" --data @body.json http://localhost:9000/tf2/ai_saas AI SaaS 服务识别结果 “Hello TensorFlow” Try

not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements not forward- compatible with CUDA 12.1. For a complete list of supported drivers, see the CUDA Application Compatibility topic. For more information, see CUDA Compatibility and Upgrades. GPU Requirements

python module for demonstrating compression. The code for this exercise is available as a Jupyter notebook here. %%capture import gzip import operator, random import numpy as np import tensorflow as kernels, and even whole channels. Libraries like XNNPACK3,4 can help accelerate networks on a variety of web, mobile, and embedded devices, provided the user can design networks that match their constraints. org/abs/1911.09723v1 3 https://github.com/google/XNNPACK Project: Lightweight model for pet filters application Recall that our regular CNN model in the pet filters project consisted of thirteen convolution

important benchmark to evaluate the performance of a deep learning model. A language translation application that uses a low quality model would struggle with consumer adoption because it wouldn’t serve its purpose of helping them communicate effectively with others who speak different languages. An application that employs a high quality model with a reasonable translation accuracy would garner better consumer pre-trained ResNet50 model and fine tune it. The code for this project is available as a Jupyter notebook here. Tensorflow provides easy access to this dataset through the tensorflow-datasets package.

Our goal is to classify a given piece of text into one of the fourteen categories. The Jupyter notebook is available here for you to play with. We have already downloaded the dataset in the dbpedia_csv params: 0 We will spare you the training logs here, but you are welcome to inspect them in the notebook directly. Figure 4-12 shows that the CNN models perform better than BOW as they benefit from the compare their training efficiency and quality metrics. As always, the code is available as a Jupyter notebook here for you to experiment. Let’s get started with loading the dataset. import tensorflow as tf

labeling. Therefore, we can simply use e-books, Wikipedia and other sources for NLU related models, and web images & videos for computer vision models. We can then construct the final dataset for the pretext downstream application (which is very reasonable), we only need to achieve that saving across 100 applications before it becomes profitable to pre-train BERT-Base rather than train each application from scratch BERT model that is trained from scratch. The code for this project is available here as a Jupyter notebook. We will not be explicitly demonstrating pre-training BERT, since as we described earlier training

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 738 16 附录：深度学习工具 741 16.1 使用Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 741 xiv 16.1.1 在本地编辑和运行代码 运行和停止实例 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.2.4 更新Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 749 16.3 使用Amazon EC2实例 Gaurav Saha, Murat Semerci, Lei Mao, Zhu Yuanxiang, thebesttv, Quanshangze Du, Yanbo Chen。 我们感谢Amazon Web Services，特别是Swami Sivasubramanian、Peter DeSantis、Adam Selipsky和Andrew Jassy对撰写本书的慷慨支持。如果没有可用的时间、资

on your computer • jupyter notebook • ???????????? Run on Princeton CS server • Pick any 4-digit number, say 1234 • ???????????? hostname -s • ???????????? jupyter notebook --no-browser --port=1234 • 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 https://github.com/szagoruyko/pytorchviz References • Important References: • For setting up jupyter notebook on princeton ionic cluster • https://oncomputingwell.princeton.edu/2018/05/jupyter-on-the-cluster/

th_AVX ��������� TensorFlow Jupyter Notebook ������� (venv) $ pip install jupyter (venv) $ python –m ipykernel install --user --name=venv � Jupyter Notebook ��� TensorFlow “Hello TensorFlow” Try tensorflow/tensorflow:nightly-jupyter 4. Start a TensorFlow Docker container $ docker run -it -p 8888:8888 -v $(notebook-examples-path):/tf/notebooks tensorflow/tensorflow:nightly-jupyter “Hello TensorFlow” Try it

??(?, ?) (3) ???(?1 + ?2, ?) = ???(?1, ?) + ???(?2, ?) 50 Python 的环境的安装 ⚫Anaconda ⚫Jupyter notebook ⚫Pycharm 详细教程：https://zhuanlan.zhihu.com/p/59027692 3. 机器学习的背景知识-Python基础 51 Python 的环境的安装 com/distribution/ 通常选3.7版本，64位 可以用默认安装，右图两个选择框都勾上 52 Python 的环境的安装 ⚫Jupyter notebook 在cmd环境下，切换到代码的 目录，输入命令： jupyter notebook之后就可以 启动jupyter botebook编辑器 ，启动之后会自动打开浏览器 ，并访问http://localhost:8088 ，默认跳转到