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Building Unsupervised Learning Models with TensorFlow
Unsupervised learning techniques work with huge data sets to find patterns within the data. This course teaches you the details of clustering and autoencoding, two versatile unsupervised learning techniques, and how to implement them in TensorFlow.
What you'll learn
Unsupervised learning techniques are powerful, but under utilized and often not well understood. In this course, Building Unsupervised Learning Models with TensorFlow, you'll learn the various characteristics and features of clustering models such as K-means clustering and hierarchical clustering. First, you'll dive into building a k-means clustering model in TensorFlow. Next, you'll discover autoencoders in detail, which are a type of artificial neural network used for unsupervised learning. Finally, you'll explore encodings or representation of data for dimensionality reduction of problems. By the end of this course, you'll have a better understanding of how you can work with unlabeled data using unsupervised learning techniques.
Table of contents
- Version Check | 20s
- Prerequisites and Required Software | 3m 33s
- Supervised Learning | 5m 55s
- Unsupervised Learning | 5m 30s
- Introduction to Clustering | 3m 8s
- Minimize Intra-cluster Similarity; Maximize Inter-cluster Similarity | 2m 11s
- The Intuition Behind How Autoencoders Work | 2m 30s
- Understanding Principal Components Analysis | 7m 5s
- Dimensionality Reducing Using Autoencoders | 5m 49s
About the author
A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.