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Explore the LLM tech stack, covering API-accessible and open-source models. Learn about ChatGLM-6B and gain insights into self-hosting options for large language models.
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.
Hands-on guide to using pre-built EG3D models on Ubuntu 22.04 with Python 3.9, PyTorch, and CuDA GPU libraries. Learn to visualize, export, and generate 3D shapes from various models.
Hands-on guide to TorchStudio: Access PyTorch datasets, train models, and compare results. Ideal for beginners and experienced users in the PyTorch ecosystem.
Learn face stylization with JoJoGAN: transfer styles to images using minimal data and GPU assistance. Covers setup, existing models, custom training, and animal face styles.
Hands-on exploration of Diffusion Models using Google Colab, covering probabilistic implementations and prebuilt models. Includes code examples and practical demonstrations of the denoising process.
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