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Hands-on workshop on data processing for satellite imagery, covering fundamentals, Python processing, image manipulation, and preparation of training data for deep learning applications.
Learn to save and retrieve prompts and images for Stable Diffusion models using Gradio. Implement a UI with dropdown lists, JSON storage, and duplication prevention for efficient prompt management.
Learn to create a Python Gradio UI for Stable Diffusion models, including audio-to-text prompts, prompt updates, and image saving functionality in this hands-on coding tutorial.
Learn to create a Python Gradio UI for Stable Diffusion models, covering UI design, prompt validation, tab creation, and integration of various functionalities in this hands-on coding tutorial.
Comprehensive guide on advanced Python library development, covering class implementation, package creation, and PyPi upload using practical examples and step-by-step instructions.
Step-by-step guide on converting a Python application to a library, covering folder structure, project setup, testing, packaging, and installation using wheel format.
Step-by-step guide on developing Python libraries, covering introduction, application conversion, and advanced concepts. Learn to create reusable code modules for efficient programming.
Learn to create dynamic user interfaces for Python applications using Gradio, with step-by-step guidance on building tabbed interfaces and practical coding examples.
Learn to create a GUI for data applications using Gradio, covering combined interfaces, tabbed layouts, validation, and integrating filters for enhanced data processing and visualization.
Learn to create a dynamic GUI for data applications using Gradio, covering installation, input components, integration with data processing, and class-based implementation.
Learn to write smarter Python code using classes for efficient data processing, including DatasetSelector and DatasetManager implementation with Scikit-learn integration.
Learn to write smarter Python code using classes for efficient data processing, including creating custom classes, adding methods, and integrating Scikit-learn for dataset management.
Learn advanced data filtering techniques in Python, including category-based filtering, handling multiple datasets, and optimizing code with inline methods for efficient data processing.
Learn Python data processing techniques including data loading, reading, and value-based filtering. Gain practical skills in handling and manipulating datasets efficiently.
Learn to implement Stable Diffusion text-to-image AI model in Python, covering setup, pipeline creation, model testing, and Gradio app development for both local and Google Colab environments.
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