 |
||||||||
t-SNE (from sklearn.manifold import TSNE) - Python for Integrated Circuits - - An Online Book - |
||||||||
| Python for Integrated Circuits http://www.globalsino.com/ICs/ | ||||||||
| Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= t-SNE (t-distributed stochastic neighbor embedding) is a popular dimensionality reduction technique in Data Science. In practice, we often have data where samples are characterized by n features. To reduce the dimensionality, t-SNE generates a lower number of features (typically two) that preserves the relationship between samples as good as possible. The scikit-learn can be used to implement t-SNE in order to achieve dimensionality reduction. The t-SNE algorithm can also be used in TensorFlow.js to reduce dimensions in an input dataset. ============================================ Find the best word similarity with Word2Vec Models/word embeddings: code:
|
||||||||
| ================================================================================= | ||||||||
|
|
||||||||
