[AI] Create a Object Recognition Web App with Python & React
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[AI] Create a Object Recognition Web App with Python & React
Build AI-driven web apps with FastAPI and React. Discover Machine Learning with Python for developers.
This comprehensive course, “[AI] Create a Object Recognition Web App with Python & React,” is designed to empower developers with the skills to build cutting-edge AI-powered applications. By combining the power of FastAPI, TensorFlow, and React, students will learn to create a full-stack object recognition web app that showcases the potential of machine learning in modern web development.
Throughout this hands-on course, participants will dive deep into both backend and frontend technologies, with a primary focus on Python for AI and backend development, and TypeScript for frontend implementation. The course begins by introducing students to the fundamentals of machine learning and computer vision, providing a solid foundation in AI concepts essential for object recognition tasks.
Students will then explore the FastAPI framework, learning how to create efficient and scalable REST APIs that serve as the backbone of the application. This section will cover topics such as request handling, data validation, and asynchronous programming in Python, ensuring that the backend can handle the demands of real-time object recognition processing.
The heart of the course lies in its machine learning component, where students will work extensively with TensorFlow to build and train custom object recognition models. Participants will learn how to prepare datasets, design neural network architectures, and fine-tune pre-trained models for optimal performance. The course will also cover essential topics such as data augmentation, transfer learning, and model evaluation techniques.
On the frontend, students will utilize React and TypeScript to create a dynamic and responsive user interface. This section will focus on building reusable components, managing application state, and implementing real-time updates to display object recognition results. Participants will also learn how to integrate the frontend with the FastAPI backend, ensuring seamless communication between the two layers of the application.
Throughout the course, emphasis will be placed on best practices in software development, including code organization and project structure. Students will also gain insights into deploying AI-powered web applications, considering factors such as model serving, scalability, and performance optimization.
By the end of the course, participants will have created a fully functional object recognition web app, gaining practical experience in combining AI technologies with modern web development frameworks. This project-based approach ensures that students not only understand the theoretical concepts but also acquire the hands-on skills necessary to build sophisticated AI-driven applications in real-world scenarios.
Whether you’re a seasoned developer looking to expand your skill set or an AI enthusiast eager to bring machine learning models to life on the web, this course provides the perfect blend of theory and practice to help you achieve your goals in the exciting field of AI-powered web development.
***DISCLAIMER*** This course is part of a 3 applications series where we build the same app with different technologies including Angular, React and a cross platform Mobile App with React Native CLI. Please choose the frontend framework that fits you best.
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1IntroductionVideo lesson
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2AI, Machine Learning and Deep LearningVideo lesson
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3Convolutional Neural Networks (CNNs)Video lesson
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4Installing VSCodeVideo lesson
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5VSCode ExtensionsVideo lesson
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6Best way to take advantage of this courseVideo lesson
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7AI, Machine Learning and Deep Learning QuizQuiz
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8What is Python and FastAPI?Video lesson
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9Installing Python for MacOSVideo lesson
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10Installing Python for WindowsVideo lesson
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11Installing and running FastAPIVideo lesson
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12Another Example RouteVideo lesson
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13Running the server with UvicornVideo lesson
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14Installing packages using requirements.txtVideo lesson
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15What is React and Typescript?Video lesson
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16Install NodeJSVideo lesson
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17Create First React App with ViteVideo lesson
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18ImageControl Component and StyleVideo lesson
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19Setting State VariablesVideo lesson
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20Predictions and Image Boxes TemplateVideo lesson
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21Image Upload InputVideo lesson
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22Explaining TensorFlow, SSD Model and Coco DatasetVideo lesson
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23Adding MobileNetV2 SSD COCO Model DataSetVideo lesson
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24Loading Pre-Trained Model into our AppVideo lesson
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25Run Inference FunctionVideo lesson
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26Predict RouteVideo lesson
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27Label MapVideo lesson
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28Returning Results From Prediction RouteVideo lesson
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29Testing Predict RouteVideo lesson
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30UseUploadImageHookVideo lesson
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31Result TypesVideo lesson
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32Returning Data from HookVideo lesson
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33Using Hook in Image ControlVideo lesson
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34API KeyVideo lesson
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35HandleUpload and HandleImageVideo lesson
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36Testing Image UploadVideo lesson
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37Allow CORSVideo lesson
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38Getting Results into ScreenVideo lesson
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