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This practice explores the use of pre-trained deep learning models from PyTorch for image classification. It includes detailed steps for using AlexNet and ResNet 101 to perform inference on images, including preprocessing, loading models, and interpreting predictions. The content is designed to understand how to leverage these models in practical applications and gain insights into their architectures.

In this project, we will use variational autoencoders to generate content, exploring their latent space.

In this project, we learn more about one of the simplest forms of generative AI, autoencoders, how it is possible to use latent space for content generation.

This API is developed in FastAPI and allows predicting the optimal crop based on soil and environmental characteristics. It uses a pre-trained machine learning model to make crop recommendations

This analysis explores the factors influencing house prices in King County, USA, based on historical sales data. The focus is on uncovering trends and insights that can inform future real estate decisions.