Resources

Starting out resources

    - Elements of Statistical Learning

    - An Introduction to Statistical Learning with Applications in R - Jaynes.

Good write-up here (though a bit dated)

Trey Causey – Getting started in data science

- ML Algo Cheat Sheet

- Keeping up resources - DeepMind, Karpathy

Perspectives

    - https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7
    - https://www.cybrhome.com/topic/data-science-blogs

- The signal and the noise by Nate Silver.

Regex (NLP)

    https://regexone.com/
    https://regex101.com/
    CheatSheet-1
    CheatSheet-2

MySQL Tutorials (from Upgrad course content)

• Grouping data
• Having clause
• Nested queries and subqueries
• Order by clause
• Natural Sorting 
• Joining tables
• Inner join
• Left join
• Union Operation 
• Intersect Operation  

- “Differences Between AI and Machine Learning and Why it Matters” by Roberto Iriondo https://link.medium.com/eW06HMJyvS

2019 IDC Predictions

1. No Pain, No Gain with enterprise AI. AI will become the innovation foundation. By 2023, compute power reqs will shoot up by 5x from 2018. 
2. Democratization of AI
3. Automation will drive new business value
4. AI is a complement, not substitute.

Topics to read in AI

1. Transfer Learning
   ftp.cs.wisc.edu/machine-learning/shavlik-group/torrey.handbook09.pdf
2. A Gentle Introduction to Transfer Learning for Deep Learning
   https://machinelearningmastery.com/transfer-learning-for-deep-learning/
3. Transfer Learning Introduction Tutorials & Notes | Machine Learning https://www.hackerearth.com/practice/machine-learning/transfer-learning/.../tutorial/
4. Transfer Learning - Deep convolutional models: case studies | Coursera
   https://www.coursera.org/lecture/convolutional-neural.../transfer-learning-4THzO

Other resources

1. https://towardsdatascience.com/
2. data.gov.in (India Public Data)
3. kaggle.org (data & competitions)
4. drivendata.org (data & competitions)
5. caseinterview.com
6. datarobot.com
7. sparkbeyond.com
8. figure8  - Data annotation platform

Deep Learning

1. http://neuralnetworksanddeeplearning.com/ (recommended by 3Blue1Brown)
2. “Essential Cheat Sheets for Machine Learning and Deep Learning Engineers” by Kailash Ahirwar https://link.medium.com/bvPR6cvwUV
3. Papers on various CNNs
• The AlexNet paper, Alex Krizhevsky et. al.
• The VGGNet paper, Karen Simonyan et. al.
• The GoogleNet, Christian Szegedy et al
• ResNet
4. ResNet Explained
• https://www.youtube.com/watch?v=RQ4sMZiciuI - This one came closest to explaining ResNet in an easily comprehensible manner. 
• https://www.youtube.com/watch?v=0tBPSxioIZE - Better
• https://www.youtube.com/watch?v=lugkZaFj4x8&t=561s - initial part of the lecture has a good visualization of the problem and the desired state.
• Why ResNets work - Andrew Ng
• Understanding and visualizing ResNets
5. Six tricks to prevent overfitting in ML Models
6. Why your Neural Network may not be performing well.
7. Combination of CNN & RNN for Sentiment Analysis of Short Texts
8. https://paperswithcode.com/sota
9. https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html
10. https://towardsdatascience.com/deciding-optimal-filter-size-for-cnns-d6f7b56f9363 
11.