NVIDIA之AI Course：Getting Started with AI on Jetson Nano—Class notes(一)

導讀
? ? ? ? 在線參加面向初學者的深度學習研究院課程，可以自由掌握進度。 將學習收集圖像數據，并用其為自定義任務（例如識別手勢以及用于找到圖像中的關鍵點的圖像回歸）訓練、優化和部署 AI 模型。 甚至可以在 Jetson Nano 上使用 Jupyter Notebooks，借助計算機視覺模型構建深度學習分類項目。
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The original text comes from?NVIDIA-AI Course. This article only provides Chinese translation.

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目錄

Getting Started with AI on Jetson Nano

Welcome

Working Through The Course

Course Outline課程大綱

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Getting Started with AI on Jetson Nano

• Welcome
• Setting up your Jetson Nano
• Image Classification
• Image Regression
• Conclusion
• Feedback

Welcome

Welcome to?Getting Started with AI on Jetson Nano! In this course, you will build AI projects on your own NVIDIA? Jetson Nano. You'll learn how to:

• Set up your Jetson Nano Developer Kit and camera to run this course
• Collect varied data for image classification projects
• Train neural network models for classification
• Annotate image data for regression
• Train neural network models for regression to localize features
• Run inference on a live camera feed with your trained models

Working Through The Course

? ? ? Throughout the course you'll work in two browser windows. The first window is the one you are viewing now. It contains the course pages you'll use for a guided learning experience, hosted on the NVIDIA? Deep Learning Institute (DLI) platform. This is where you'll find instructions, references, and quizzes. You can also track your progress toward earning a Certificate of Competency for the course.
? ? ? The second browser window contains a remote JupyterLab interface into your Jetson Nano. You'll begin with some hardware setup in the Setting up your Jetson Nano section, and then open this window in your computer browser. This JupyterLab window is where you'll run Python code interactively in Jupyter notebooks to view the camera feed and build your AI Classification and Regression projects. The Jupyter notebooks you'll work with are easy to copy, change, experiment with, and extend for your own additional projects whenever you are ready to do so!
? ? ? Let's get started! The following video provides a brief overview of the Jetson Nano Developer Kit product.

? ? ? ??在整個課程中，您將在兩個瀏覽器窗口中工作。第一個窗口是您現在正在查看的窗口。它包含在Nvidia?深度學習學院（DLI）平臺上舉辦的引導式學習體驗課程頁面。在這里您可以找到說明、參考資料和測驗。您還可以跟蹤您獲得課程合格證書的進度。
?? ? ? ??第二個瀏覽器窗口在Jetson nano中包含遠程jupyterlab界面。您將從設置Jetson nano部分中的一些硬件設置開始，然后在計算機瀏覽器中打開此窗口。在這個jupyterlab窗口中，您將在jupyter筆記本中交互運行python代碼，以查看攝像頭提要并構建人工智能分類和回歸項目。你將要使用的Jupyter筆記本很容易復制、更改、實驗，并且在你準備好的時候擴展到你自己的附加項目中。
?? ? ? ??我們開始吧！以下視頻簡要概述了Jetson nano開發工具包產品。

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Course Outline課程大綱

The course consists of three main sections. Use the navigation and breadcrumb links at the top of each section to step through the lessons.
該課程由三個主要部分組成。使用每個部分頂部的導航和breadcrumb鏈接來逐步完成課程。

1.?Setting Up Your Jetson Nano
Step-by-step guide to set up your hardware and software for the course projects
一步一步的指導，設置您的硬件和軟件的課程項目。

• Introduction：What's included with the Jetson Nano Developer Kit
• Prepare for Setup：Descriptions of additional hardware you need to get started
• Write Image to the MicroSD Card：How to download the software for this course and make it available to the Jetson Nano Developer Kit
• Setup and First Boot：Illustrated step-by-step instructions to boot your Jetson Nano with the complete OS image and course software
• Camera Setup：How to connect your camera to the Jetson Nano Developer Kit
• Hello Camera：How to test your camera with an interactive Jupyter notebook on the Jetson Nano Developer Kit
• JupyterLab：A brief introduction to the JupyterLab interface and notebooks

2. Image Classification圖像分類
Background information and instructions to create projects that classify images using Deep Learning
創建使用深度學習對圖像進行分類的項目的背景信息和說明。

• AI and Deep Learning：A brief overview of Deep Learning and how it relates to Artificial Intelligence (AI)
• Convolutional Neural Networks (CNNs)：An introduction to the dominant class of artificial neural networks for computer vision tasks
• ResNet-18：Specifics on the ResNet-18 network architecture used in the class projects
• Thumbs Project：Work with the Interactive Classification notebook to create your first project
• Emotions Project：Build a new project with the same Interactive notebook to detect emotions from facial expressions.建立一個新的項目相同的交互式筆記本檢測情緒面部表情。
• Quiz Questions：Answer questions about what you've learned to reinforce your knowledge.回答問題對你所學到的知識鞏固你的知識

3. Image Regression 圖像回歸
Instructions to create projects that can localize and track image features in a live camera image. 介紹創建一個項目，可以本地化和跟蹤實時攝像機圖像中的圖像功能。

• Classification vs. Regression：With a few changes, your the Classification model can be converted to a Regression model.用一些小的改變，你的分類模型可以轉化成一個回歸模型。
• Face XY Project：Build a project that finds the coordinates of facial features.建立一個項目,發現面部特征的坐標
• Quiz Questions：Answer questions about what you've learned to reinforce your knowledge.回答問題對你所學到的知識鞏固你的知識

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