NVIDIA之AI Course：Getting Started with AI on Jetson Nano—Class notes(一)
NVIDIA之AI Course：Getting Started with AI on Jetson Nano—Class notes(一)
導(dǎo)讀
? ? ? ? 在線參加面向初學(xué)者的深度學(xué)習(xí)研究院課程，可以自由掌握進(jìn)度。 將學(xué)習(xí)收集圖像數(shù)據(jù)，并用其為自定義任務(wù)（例如識(shí)別手勢(shì)以及用于找到圖像中的關(guān)鍵點(diǎn)的圖像回歸）訓(xùn)練、優(yōu)化和部署 AI 模型。 甚至可以在 Jetson Nano 上使用 Jupyter Notebooks，借助計(jì)算機(jī)視覺(jué)模型構(gòu)建深度學(xué)習(xí)分類(lèi)項(xiàng)目。
Notice
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(gè)課程中，您將在兩個(gè)瀏覽器窗口中工作。第一個(gè)窗口是您現(xiàn)在正在查看的窗口。它包含在Nvidia?深度學(xué)習(xí)學(xué)院（DLI）平臺(tái)上舉辦的引導(dǎo)式學(xué)習(xí)體驗(yàn)課程頁(yè)面。在這里您可以找到說(shuō)明、參考資料和測(cè)驗(yàn)。您還可以跟蹤您獲得課程合格證書(shū)的進(jìn)度。
?? ? ? ??第二個(gè)瀏覽器窗口在Jetson nano中包含遠(yuǎn)程jupyterlab界面。您將從設(shè)置Jetson nano部分中的一些硬件設(shè)置開(kāi)始，然后在計(jì)算機(jī)瀏覽器中打開(kāi)此窗口。在這個(gè)jupyterlab窗口中，您將在jupyter筆記本中交互運(yùn)行python代碼，以查看攝像頭提要并構(gòu)建人工智能分類(lèi)和回歸項(xiàng)目。你將要使用的Jupyter筆記本很容易復(fù)制、更改、實(shí)驗(yàn)，并且在你準(zhǔn)備好的時(shí)候擴(kuò)展到你自己的附加項(xiàng)目中。
?? ? ? ??我們開(kāi)始吧！以下視頻簡(jiǎn)要概述了Jetson nano開(kāi)發(fā)工具包產(chǎn)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(gè)主要部分組成。使用每個(gè)部分頂部的導(dǎo)航和breadcrumb鏈接來(lái)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(dǎo)，設(shè)置您的硬件和軟件的課程項(xiàng)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(lèi)
Background information and instructions to create projects that classify images using Deep Learning
創(chuàng)建使用深度學(xué)習(xí)對(duì)圖像進(jìn)行分類(lèi)的項(xiàng)目的背景信息和說(shuō)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(gè)新的項(xiàng)目相同的交互式筆記本檢測(cè)情緒面部表情。
- Quiz Questions：Answer questions about what you've learned to reinforce your knowledge.回答問(wèn)題對(duì)你所學(xué)到的知識(shí)鞏固你的知識(shí)
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(chuàng)建一個(gè)項(xiàng)目，可以本地化和跟蹤實(shí)時(shí)攝像機(jī)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(lèi)模型可以轉(zhuǎn)化成一個(gè)回歸模型。
- Face XY Project：Build a project that finds the coordinates of facial features.建立一個(gè)項(xiàng)目,發(fā)現(xiàn)面部特征的坐標(biāo)
- Quiz Questions：Answer questions about what you've learned to reinforce your knowledge.回答問(wèn)題對(duì)你所學(xué)到的知識(shí)鞏固你的知識(shí)
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