Introducing pandasai generative ai python library

introduction to pandasai generative ai python library

pandasai is a python library for building generative artificial intelligence models using deep learning techniques. this library provides tools and utilities to easily create and train generative models such as gans (generative adversarial networks) and vaes (variational autoencoders). with pandasai, you can generate new data samples that resemble the input data distribution, which is useful for tasks like image generation, text generation, and more.

key features of pandasai:
1. simplified api for building and training generative models.
2. supports various types of generative models like gans, vaes, and more.
3. flexible customization options for model architecture and training parameters.
4. integration with popular deep learning frameworks like tensorflow and pytorch.

now, let's see a simple code example using pandasai to create and train a gan model for generating images:

in this example, we first load the mnist dataset using the `load_dataset` function. then, we create a gan model with an input shape of (28, 28, 1) for generating images. we compile and train the gan model on the mnist dataset for 50 epochs. finally, we generate 10 new images using the trained model and display them using matplotlib.

this is just a basic example to demonstrate how to use pandasai for building generative ai models. you can explore more advanced features and customization options provided by the library to create more complex generative models for your specific tasks.

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