STUMPY v1.4.0 released for modern time series analysis

https://stumpy.readthedocs.io/en/latest/

Haptik open sourced a NLP toolkit (multi-task-NLP) to help train multiple models in 3 simple steps without the need to code.

1. (Query-passage) Answerability prediction
2. Textual entailment (can be used for question answering, adversarial query detection)
3. Intent detection + Named entity detection + Fragment detection (Multi - task setup)
4. Named entity recognition + part of speech tagging ( Multi-task )
5. Query grammatical correctness
6. Query pair similarity (QnA)
7. Query type detection
8. Sentiment analysis

Github : https://lnkd.in/dGmYzVT
Documentation : https://lnkd.in/duRX4fS

Day 35 resources -

1. https://www.saedsayad.com/k_nearest_neighbors.htm#:~:text=K%20nearest%20neighbors%20is%20a,as%20a%20non%2Dparametric%20technique.

2. https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/

3. https://www.analyticsvidhya.com/blog/2018/03/introduction-k-neighbours-algorithm-clustering/

4.https://www.youtube.com/watch?v=HVXime0nQeI

Day 37 resources.

1. https://machinelearningmastery.com/how-to-identify-outliers-in-your-data/

2. https://www.neuraldesigner.com/blog/3_methods_to_deal_with_outliers

3. https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/

4. https://heartbeat.fritz.ai/how-to-make-your-machine-learning-models-robust-to-outliers-44d404067d07

5. https://towardsdatascience.com/ways-to-detect-and-remove-the-outliers-404d16608dba

6. https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/

7. https://medium.com/@swethalakshmanan14/outlier-detection-and-treatment-a-beginners-guide-c44af0699754

3D reconstruction from a single RGB image

Github - https://github.com/zhou13/symmetrynet

Paper - https://arxiv.org/pdf/2006.10042.pdf

10 Wonderful Examples Of Using Artificial Intelligence (AI) For Good
https://www.forbes.com/sites/bernardmarr/2020/06/22/10-wonderful-examples-of-using-artificial-intelligence-ai-for-good/

Day - 39 Resources:

1. https://towardsdatascience.com/understanding-feature-engineering-part-2-categorical-data-f54324193e63

2. https://www.datacamp.com/community/tutorials/categorical-data

3.https://www.analyticsvidhya.com/blog/2015/11/easy-methods-deal-categorical-variables-predictive-modeling/

4. https://machinelearningmastery.com/how-to-prepare-categorical-data-for-deep-learning-in-python/

5. https://medium.com/hugo-ferreiras-blog/dealing-with-categorical-features-in-machine-learning-1bb70f07262d

6.https://medium.com/data-design/visiting-categorical-features-and-encoding-in-decision-trees-53400fa65931

7.

Given access please check

9 Free Online Resources To Learn Transfer Learning
https://analyticsindiamag.com/9-free-resources-to-learn-transfer-learning/