Learn how to build machine learning and deep learning models for many purposes in Python using popular frameworks such as TensorFlow, PyTorch, Keras and OpenCV.
Learn how to perform vehicle detection, tracking and counting with YOLOv8 and DeepSORT using OpenCV library in Python.
Learn how to use stable diffusion 4x upscaler to upscale your low-resolution images into high quality images with Huggingface transformers and diffusers libraries in Python.
Learn how you can edit and style images using Instruct-Pix2Pix with the help of Huggingface diffusers and transformers libraries in Python.
Learn how you can control images generated by stable diffusion using ControlNet with the help of Huggingface transformers and diffusers libraries in Python.
Learn the current state-of-the-art models (such as BLIP, GIT, and BLIP2) for visual question answering with huggingface transformers library in Python.
Learn how you can fine-tune BERT or any other transformer model for semantic textual similarity using Huggingface Transformers, PyTorch and sentence-transformers libraries in Python.
Learn how to perform real-time object tracking with the DeepSORT algorithm and YOLOv8 using the OpenCV library in Python.
Learn how you can generate similar images with depth estimation (depth2img) using stable diffusion with huggingface diffusers and transformers libraries in Python.
Learn how to perform text-to-image using stable diffusion models with the help of huggingface transformers and diffusers libraries in Python.
Learn how to fine tune the Vision Transformer (ViT) model for the image classification task using the Huggingface Transformers, evaluate, and datasets libraries in Python.
Learn how to use image segmentation transformer model to segment any image using huggingface transformers and PyTorch libraries in Python.
Learn how to use pre-trained image captioning transformer models and what are the metrics used to compare models, you'll also learn how to train your own image captioning model with Pytorch and transformers in Python.
This article is a must-read for anyone looking to unlock the full potential of clustering in machine learning! It delves into the world of clustering, exploring different types such as density-based and centroid-based, and introducing lesser-known techniques like hierarchical and monothetic clustering with Python.