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

 跨场景+跨模态  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 Solutions Continuous Optimization: Active learning Data Label Model Serving CV / NLP解决方案: EAS Web App Mobile App On-prem System 3 1 2 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

装 的程序组件。在 CUDA 节点下，取消”Visual Studio Integration”一项；在“Driver 预览版202112 1.6 开发环境安装 19 components”节点下，比对目前计算机已经安装的显卡驱动“Display Driver”的版本号 “Current Version”和 CUDA 自带的显卡驱动版本号“New Version”，如果“Current # 清零测量器 8.7 可视化 在网络训练的过程中，通过 Web 端远程监控网络的训练进度，可视化网络的训练结 果，对于提高开发效率和实现远程监控是非常重要的。TensorFlow 提供了一个专门的可视 化工具，叫做 TensorBoard，它通过 TensorFlow 将监控数据写入到文件系统，并利用 Web 后端监控对应的文件目录，从而可以允许用户从远程查看网络的监控数据。 在运行程序时，监控数据被写入到指定文件目录中。如果要实时远程查看、可视化这 些数据，还需要借助于浏览器和 Web 后端。首先是打开 Web 后端，通过在 cmd 终端运行 tensorboard --logdir path 指定 Web 后端监控的文件目录 path，即可打开 Web 后端监控进 程，如图 8.2 所示： 图 8.2 启动 Web 服务器 此时打开浏览器，并输入网址 http://localhost:6006

PyTorch, a model is represented by a regular Python class that inherits from the Module class. • Two components • __init__(self): it defines the parts that make up the model —in our case, two parameters, a kind of a Python list of tuples, each tuple corresponding to one point (features, label) • 3 components: • __init__(self) • __get_item__(self, index) • __len__(self) • Unless the dataset is huge

decomposition 模块包含了一系列无监督降维算法 from sklearn.decomposition import PCA 导入PCA库，设置主成分数量为3，n_components代表主成分数量 pca = PCA(n_components=3) 训练模型 pca.fit(X) 投影后各个特征维度的方差比例(这里是三个主成分) print(pca.explained_variance_ratio_)

human designed models. However, the key contribution of NASNet was the focus on predicting the components of child networks which enabled the construction of multiscale networks without needing to tweak ), dict(name='combinations', values=['add', 'concat'], count=1), ] The STATE_SPACE has three components to mimic the NASNet search space. The hidden_state element can take two values to represent the

compression, and the MP3 format for audio. DCT breaks down the given input data into independent components of which the ones that don’t contribute much to the original input can be discarded, based on the graph using the 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

inference. Figure 1-17: Model Training & Inference stages, along with the constituent infrastructure components. Advances in hardware are significantly responsible for the deep learning revolution, specifically

construct the features by hand (at least in the pre deep learning era). Techniques like Principal Components Analysis, Low-Rank Matrix Factorization, etc. are popular tools for dimensionality reduction. We

networks. 1. Keras ― Introduction Keras 2  Deep learning models are discrete components, so that, you can combine into many ways. Keras 3 This chapter explains