#Unsupervised Learning

Explore a structured 100-day journey into machine learning, covering both supervised and unsupervised techniques. This project involves practical coding exercises in algorithms such as linear regression, k-NN, and support vector machines (SVMs). With resources from Coursera and YouTube, enhance your understanding of key concepts like decision trees and neural networks, fostering a comprehensive grasp of machine learning.

This collection gathers a wide range of papers and code concerning Generative Adversarial Networks (GANs), offering insights into applications such as image translation and facial attribute manipulation. It also delves into theoretical perspectives and machine learning methods with interdisciplinary applications in fields like medicine and music. Featuring advancements in autoML, image animation, and GAN theory, this repository serves researchers and developers interested in exploring GAN technology comprehensively. This resource ensures an extensive understanding of GANs' impact across varied domains.

Discover how Vision Transformers, including TransMorph, revolutionize medical image registration for enhanced precision. Evaluate different TransMorph variants and benchmark comparisons to gain insight into performance. Find guidance on Docker setups for brain MRI registration and access reproducible results from datasets like IXI and OASIS. Perfect for researchers interested in advanced transformer methods for unsupervised image alignment, equipped with pre-trained models and comprehensive training documentation.

Gain understanding of the archived GPT-2 project from OpenAI, known for introducing unsupervised multitask language models. This resource offers code, models, and a dataset useful for researchers and engineers studying model behavior. Learn about key concerns like model robustness and biases, and why careful use is critical in safety-sensitive applications. Opportunities exist for contributions in studying bias reduction and synthetic text detection, building on the foundational work for future progress.