AI, Python, Cognitive Neuroscience
3.8K subscribers
1.09K photos
46 videos
78 files
891 links
Download Telegram
Kaggle kernels that Researcher have published.

1. Time Series Analysis - Artificial Neural Networks - https://lnkd.in/f8diQkX

2. Titanic - Data Preprocessing and Visualization - https://lnkd.in/fwrvHr5

3. Everything you can do with Seaborn - https://lnkd.in/fpgQCr8

4. Insights of Kaggle ML and DS Survey - https://lnkd.in/fPyiGyU

5. Time Series Analysis - ARIMA model - https://lnkd.in/fn24ihz

6. Time Series Analysis - LSTM - https://lnkd.in/fuY6DXm

7. Introduction to Regression - Complete Analysis - https://lnkd.in/fM3xsZ2

8. Time Series - Preprocessing to Modelling - https://lnkd.in/fJcar4u

Kaggle community is one of the best community for Data Science.

#machinelearning #artificialintelligence #datascience #deeplearning #data

✴️ @AI_Python_EN
🗣 @AI_Python_arXiv
❇️ @AI_Python
How would a rockstar 🎸would improve their machine learning models?
چگونه مدلهای یادگیری ماشین را بهبود دهیم؟

To get better at playing the guitar, you play the guitar more. You try different songs, different cords. Practice, practice, practice.

All the practice adds up to more experience, more examples of different notes.

And to try something totally different, you might merge two songs together. Or even take a song written originally for the piano but play it on your guitar.

After a while, you're ready to play a show. But the show won't some any good if all the speakers are set to different settings. Steve the sound guy takes care of this.

How does this relate #machinelearning?

1. More practice = more data

More examples of playing different notes = more data. Machine learning models love more data.

2. Combining different songs = feature engineering

If the #data you have isn't in the form you want, transforming into a different shape may be a better way of looking at it.

3. Tuning the speakers = hyperparameter tuning

There's a reason tuning the speakers is the last step in playing a rock show. Working speakers don't mean anything without all the practice (collecting data) and songwriting (feature engineering). If you've done 1 and 2 right, this is the easy part.

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN