TensorFlow: Building Autoencoders
TensorFlow
| Intermediate
- 10 Videos | 46m 2s
- Includes Assessment
- Earns a Badge
Explore how to perform dimensionality reduction using powerful unsupervised learning techniques such as Principal Components Analysis and autoencoding.
WHAT YOU WILL LEARN
-
recognize how patterns help encode data
define how autoencoders work
recognize how principal component analysis works for dimensionality reduction
process data to perform principal component analysis
implement dimensionality reduction using principal component analysis with scikit-learn -
apply autoencoders to perform principal component analysis
identify how to use the Fashion MNIST dataset for dimensionality reduction
apply autoencoders to images to reconstruct them from lower dimensionality representations
define how autoencoders work and their use cases
IN THIS COURSE
-
1.Course Overview1m 40sUP NEXT
-
2.Efficient Representation of Data Using Encodings2m 43s
-
3.Autoencoders7m 31s
-
4.Principal Component Analysis6m 35s
-
5.Performing Principal Component Analysis on Datasets5m 44s
-
6.Principal Component Analysis with scikit-learn3m 25s
-
7.Autoencoders for Principal Component Analysis5m 43s
-
8.The Fashion MNIST Dataset4m 11s
-
9.Autoencoders for Dimensionality Reduction5m 41s
-
10.Exercise: Working with Autoencoders2m 49s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform
Digital badges are yours to keep, forever.
