Timestamp | Description |
---|---|
00:00:00 | Word Vectors |
00:00:37 | One-Hot Encoding and its shortcomings |
00:02:07 | What embeddings are and why they're useful |
00:05:12 | Similar words share similar contexts |
00:06:15 | Word2Vec, a way to automatically create word embeddings |
00:08:08 | Skip-Gram With Negative Sampling (SGNS) |
00:17:11 | Three ways to use word vectors in models |
00:18:48 | DEMO: Training and using word vectors |
00:41:29 | The weaknesses of static word embeddings |