Day 31 resources -

1. https://towardsdatascience.com/understanding-logistic-regression-9b02c2aec102

2. https://www.youtube.com/watch?v=yIYKR4sgzI8

3. https://www.youtube.com/watch?v=vN5cNN2-HWE

4. https://www.youtube.com/watch?v=BfKanl1aSG0

5. https://www.youtube.com/watch?v=xxFYro8QuXA

6. https://www.datacamp.com/community/tutorials/understanding-logistic-regression-python

List of Awesome Open Source Machine Learning Repos
https://towardsdatascience.com/list-of-awesome-open-source-machine-learning-repos-537fdc08ad4d

Semantic Photo Manipulation with a Generative Image Prior

http://ganpaint.io/

BERT NLP — How To Build a Question Answering Bot
https://towardsdatascience.com/bert-nlp-how-to-build-a-question-answering-bot-98b1d1594d7b

IIT Kharagpur Offers Free Online Course on Deep Learning for Engineers through NPTEL
https://www.dqindia.com/iit-kharagpur-offers-free-online-course-deep-learning-engineers-nptel/

Research paper

Day 33 resources -

1. https://www.youtube.com/watch?v=TP9W7hmb0Bs

2. https://www.youtube.com/watch?v=RublDm4J1vY

3. https://towardsdatascience.com/hyperparameter-tuning-explained-d0ebb2ba1d35

4. https://www.jeremyjordan.me/hyperparameter-tuning/

5. https://www.coursera.org/lecture/competitive-data-science/hyperparameter-tuning-i-giBKx

6.https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a

7.https://towardsdatascience.com/hyper-parameter-tuning-techniques-in-deep-learning-4dad592c63c8

The latest version of AI App by Nvidia generates a seemingly infinite number of portraits in any painting styles

link - https://news.developer.nvidia.com/synthesizing-high-resolution-images-with-stylegan2/

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