Generative AI: Python Libraries and Techniques

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Generative AI, or Generative Artificial Intelligence, refers to a class of artificial intelligence techniques and models that aim to generate new, original content. Unlike discriminative models that focus on classifying input data into predefined categories, generative models focus on creating new data that resembles a given dataset.

One key application of generative AI is in the generation of realistic content such as images, text, music, and more.

I. Libraries and Frameworks:

1. TensorFlow and Keras:

• Description: TensorFlow is an open-source machine learning library, and Keras is a high-level neural networks API that runs on top of TensorFlow. Together, they provide a powerful platform for building and training generative models.
• Use Cases: GANs, VAEs, and various deep learning models.

2. PyTorch:

• Description: PyTorch is another popular deep learning framework with dynamic computational graphs. It is widely used for building and training generative models.
• Use Cases: GANs, VAEs, and Transformers for sequence generation.

3. Generative Adversarial Network (GAN) Libraries: