#t-SNE

Master image embeddings and vector analysis techniques like CLIP, T-SNE, and UMAP. Learn to cluster MNIST images, detect duplicates, and explore essential concepts in computer vision and data science through hands-on practice.

Learn to build and train a neural network classifier using JAX, including weight visualization, embedding analysis with t-SNE, and dead neuron detection.

Explore dimensional reduction using PaCMAP algorithm, from basic implementation to advanced applications with MNIST datasets, featuring hands-on Python coding in Colab notebooks.

Explore unsupervised learning techniques for high-dimensional data analysis, focusing on matrix factorization methods and their applications in oncology and precision medicine.

Comprehensive overview of dimension reduction techniques, from linear methods like PCA to nonlinear approaches such as LLE, Isomap, t-SNE, and UMAP, with practical application to chemical reaction data.

Explore unsupervised learning applications in analyzing AC susceptibility measurements of high-temperature superconductors, focusing on clustering techniques and data representation methods.