Generative AI Resources
Welcome to the Generative AI repository! This repository provides resources, tutorials, and examples related to generative artificial intelligence.
Generative AI focuses on creating models that can generate new content, such as images, text, and music, based on the patterns learned from training data.
Overview This repository includes:
Tutorials: Step-by-step guides on understanding and implementing generative AI models.
Examples: Code examples and projects showcasing generative AI applications.
Models: Pre-trained generative models for various tasks.
Datasets: Datasets used for training generative models.
Scripts: Python scripts for training and evaluating generative AI models.
Set Up Your Environment: Install the required libraries and dependencies.
Use pip or conda to set up your environment. Common libraries for generative AI include:
pip install numpy pandas tensorflow keras torch torchvision matplotlib
Explore the Content: Navigate through the repository to find tutorials, examples, and scripts.
Open the Jupyter notebooks or Python scripts to view the content and run the code.
Run the Examples: Execute the provided scripts or notebooks to see how generative models are implemented and trained.
Modify the code to experiment with different settings and parameters.
Use the Models: Load and use pre-trained generative models for tasks such as text generation, image synthesis, or music composition.
Check the documentation for details on how to apply these models.
Key Topics
Generative Adversarial Networks (GANs): Techniques for training GANs to generate realistic images and other data.
Variational Autoencoders (VAEs): Methods for using VAEs to create new data samples and perform data reconstruction.
Transformers and Attention Mechanisms: Application of transformers in generating text and other sequential data.
Diffusion Models: Understanding and implementing diffusion models for generating high-quality samples.
Applications: Real-world applications of generative AI, such as image style transfer, text-to-image synthesis, and more.
Requirements
Python 3.x
Jupyter Notebook (optional, for running notebooks) Libraries: TensorFlow, Keras, PyTorch, NumPy, Pandas, Matplotlib (install via pip)