Unsupervised learning is a key area in AI that focuses on identifying patterns and relationships in data without being explicitly guided by labels or target outputs. Below are some valuable resources to help you dive deeper into the world of unsupervised learning.
Books
- "Unsupervised Learning: Foundations of Neural Computation" by L. Jackel This book provides a comprehensive introduction to the theoretical foundations of unsupervised learning.
Online Courses
- Coursera - Unsupervised Learning by Andrew Ng This course, taught by the renowned AI expert Andrew Ng, covers the basics of unsupervised learning and its applications.
Tutorials
- TensorFlow Tutorial: Clustering with K-means Learn how to use K-means clustering with TensorFlow to identify patterns in your data.
Research Papers
- "K-means++: The Advantages of Careful Seeding" by David Arthur and Sergei Vassilvitskii This paper discusses the improvements made to the K-means clustering algorithm and its effectiveness.
Communities
- Reddit - r/MachineLearning Join this community to discuss the latest trends and ask questions about unsupervised learning.
K-means Clustering
Further Reading
For more in-depth learning, you might want to explore the following topics: