Some really cool blogs/guides compiled over the years

1. Overfitting explained simply

2. Leetcode for Data Science

3. Fundamentals of ML, IITD

4. Why read, a blog by an alum of 2013-17 batch

5. ML Primer, using xkcd templates

6. DL Playlist by Prof Bryce, must watch alongside IDL by Goodfellow

7. Ensemble Learning

8. Revise ML through Stanford cheatsheets

9. Activation Functions
   • Comparing activations, link broken perhaps
   • Short discussion on ReLU and Sigmoid

10. Batch Norm explained

11. AutoEncoders and Variational Auto Encoders
    • Watch vids 44 - 50
    • Variational AutoEncoders
    • Paper, Tutorial on VAE
12. Do we Use Dropouts with CNN’s ?
    • Experimenting with networks
    • KdNuggets Blog

13. Deep Dream. Firing select neurons in the network and propogate changes to the input image, gives weird but interesting patterns.

14. Exhaustive Blog on LSTMs

15. Enforcing Constraints at Amazon. Really cheeky way to enforce constraints.

• If a package size had no reported damage for the product, dummy datapoints were added to the DB such that even for bigger package sizes no damage was reported.
• Similarly to enforce the negative constraint, if damage was reported for a particular size, all sizes below it were added in the DB with damage = True