Udemybeginnerja

7ステップで作るPython x Flask x Pytorch 人工知能Webアプリ開発入門

Learn image recognition with Vision Transformers (ViT)! Build your own image recognition app using this powerful AI technique. Practical ViT implementation.

Related:AI ProjectReact DevelopmentModel Deployment
4.0(137)
1,055students
2h 22m
Certificate
経知 神草
経知 神草AI/agent/chatgpt/Manus/dify/gemini cli/cline/claude code

Course Content

7ステップで作るPython x Flask x Pytorch 人工知能Webアプリ開発入門 is a beginner-level Udemy course taught by 経知 神草. It runs 2h 22m, is taught in Japanese, and includes a certificate of completion. Learners rate it 4.0 out of 5 from 137 reviews, with 1,055 students enrolled. It is a paid course, last updated March 2023.

Build Cutting-Edge AI Image Recognition Web Applications

This hands-on tutorial guides you through creating a powerful artificial intelligence (AI) image recognition web application from start to finish. You'll gain practical skills, from setting up your development environment to deploying a sophisticated AI solution.

What You'll Learn:

  • Environment Setup: Master setting up your development environment using Miniconda and VSCode.
  • Web Application Fundamentals: Build a robust web application using Python, Flask, Jinja2, HTML, CSS, jQuery, JavaScript, and Bootstrap.
  • AI for Image Recognition: Dive into the world of deep learning for image recognition with Jupyter Notebook, PyTorch, ViT (Vision Transformer), TorchVision, and Pillow.
  • State-of-the-Art Image Recognition: Explore the latest advancements in image recognition, including the revolutionary Vision Transformer (ViT), which has surpassed traditional CNN (Convolutional Neural Networks) in accuracy. Learn how to implement ViT models for superior performance.
  • Rapid Development: Discover how to implement a ViT image recognition web app in just 2.0 hours using open-source libraries like Python and jQuery. No expert coding skills required!
  • UI/UX Enhancement: Improve your application's user interface with Bootstrap and CSS.
  • File Handling: Learn to manage files effectively using FileAPI.

Course Curriculum:

  • Step 1: Development Environment Setup (Miniconda/VSCode) - Get your development environment ready.
  • Step 2: Flask (Jinja2) Basics - Learn the fundamentals of building web applications with Flask and Jinja2.
  • Step 3: jQuery/HTML Image Uploader - Implement a user-friendly image upload feature.
  • Step 4: Image Preprocessing & ViT (Vision Transformer) in Jupyter Notebook - Master image preprocessing techniques and implement the ViT model using PyTorch, TorchVision, and Pillow.
  • Step 5: Calling ViT (Vision Transformer) from Flask/Jinja2 - Integrate the ViT model into your Flask web application.
  • Step 6: UI Improvement with Bootstrap/CSS - Enhance the user interface with Bootstrap and CSS styling.
  • Step 7: Final Touches with FileAPI - Add final functionality using FileAPI.

Who is this course for?

This course is perfect for aspiring AI web developers, machine learning engineers, and anyone interested in building practical AI applications. You'll gain the skills to prototype high-performance AI web applications and even launch your own AI services.

Instructor:

Learn from an experienced machine learning web engineer who will guide you through every step of the process and answer your questions. Don't hesitate to ask!

Unlock the power of AI image recognition and build the next generation of intelligent web applications.

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