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[Ai] Create A Object Recognition Web App With Python & React
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.57 GB | Duration: 2h 58m
Build AI-driven web apps with FastAPI and React. Discover Machine Learning with Python for developers.
What you'll learn
AI and Machine Learning Fundamentals with hands on
Basic Programming in Python and Typescript
Handle frameworks like FastAPI and React
Build real world modern object recognition application
Requirements
No programming experience required. Only computer and access to internet
Description
[AI] Create a Object Recognition Web App with Python & ReactBuild 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.Cover designed by FreePik
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 AI, Machine Learning and Deep Learning
Lecture 3 Convolutional Neural Networks (CNNs)
Lecture 4 Installing VSCode
Lecture 5 VSCode Extensions
Lecture 6 Best way to take advantage of this course
Section 2: FastAPI and Python Setup
Lecture 7 What is Python and FastAPI?
Lecture 8 Installing Python for MacOS
Lecture 9 Installing Python for Windows
Lecture 10 Installing and running FastAPI
Lecture 11 Another Example Route
Lecture 12 Running the server with Uvicorn
Lecture 13 Installing packages using requirements.txt
Section 3: React Application Setup
Lecture 14 What is React and Typescript?
Lecture 15 Install NodeJS
Lecture 16 Create First React App with Vite
Lecture 17 ImageControl Component and Style
Lecture 18 Setting State Variables
Lecture 19 Predictions and Image Boxes Template
Lecture 20 Image Upload Input
Section 4: Creating and Setting Prediction Model
Lecture 21 Explaining TensorFlow, SSD Model and Coco Dataset
Lecture 22 Adding MobileNetV2 SSD COCO Model DataSet
Lecture 23 Loading Pre-Trained Model into our App
Lecture 24 Run Inference Function
Lecture 25 Predict Route
Lecture 26 Label Map
Lecture 27 Returning Results From Prediction Route
Lecture 28 Testing Predict Route
Section 5: Adding Serve Data to FrontEnd
Lecture 29 UseUploadImageHook
Lecture 30 Result Types
Lecture 31 Returning Data from Hook
Lecture 32 Using Hook in Image Control
Lecture 33 API Key
Lecture 34 HandleUpload and HandleImage
Lecture 35 Testing Image Upload
Lecture 36 Allow CORS
Lecture 37 Getting Results into Screen
Section 6: Additional Lectures
Lecture 38 Splitting FrontEnd into smaller components
Lecture 39 React Props
Lecture 40 Use cases and limitations
Beginner Python, Frontend and AI developers. Students with interest in how AI works
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