Notice
Recent Posts
Recent Comments
Link
| 일 | 월 | 화 | 수 | 목 | 금 | 토 |
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 |
| 8 | 9 | 10 | 11 | 12 | 13 | 14 |
| 15 | 16 | 17 | 18 | 19 | 20 | 21 |
| 22 | 23 | 24 | 25 | 26 | 27 | 28 |
| 29 | 30 |
Tags
- bias vs variance
- color and data
- plotting
- machine learning
- NO TYPO
- Pararel Computing
- sweet spot in fitting
- level of positive
- Learn from mistake
- ML
- color theory
- API
- Apache Beam
- root of the word
- Insight
- utilize
- level of emotion
- distributed training
- Naive Bayes example
- variance bias
- tensorflow
- NLP
- pandas
- Data Distribution
- Deep Learning
- negotiate
- plot color
- level of negative
- online machine learning
- productivity
Archives
- Today
- Total
목록Internship (1)
How much am I expressed through the data
stemming
' However, in some cases, the stemming process produces words that are not correct spellings of the root word.' ' ex) happi and sunni. That's because it chooses the most common stem for related words. -- ' 1. In terms of probablities ? (I meant to say observed cases.) #Copyrights:This screenshot is part of the Coursera's Natural Language Processing Specialization by DeepLearning.ai
Internship/UMichigan Research - 2021 Summer
2021. 12. 24. 11:25