All Technical Notes I Could think of

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• Written by me
• Others
• Academics at IIIT Jabalpur

Ƹ̵̡Ӝ̵̨̄Ʒ Ƹ̵̡Ӝ̵̨̄Ʒ Ƹ̵̡Ӝ̵̨̄Ʒ
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Written by me

1. Getting Started With ML

2. What even are RAGs Saw Sony Research asking for RAGs for undergrad research positions, hence this.

3. Positional Encoding in Transformers

4. IDL by Alexander Amini Notes

5. Initializations in Deep Learning

6. Summary of some Netflix Tech Blogs

7. Exploring Recurrent Neural Networks, will be shifted to the repo soon

8. BSoC Summary Sheet, whish was a fun toned down ML Primer for sophomores

9. Linear Least Squares Method

10. Stock Price Regressor at IITK

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Others that I really liked

Most of these have been put up on the college discord server under the channel “#data-n-stuff”

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

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Troubled by Academics ?

Check out past year papers, compiled and maintained by BitByte (hopefully)

Academic Info Page Academic Question Bank, is not revised periodically

1. SEM-1
   Acad Talk

2. SEM-2
   Acad Talk

3. SEM-3
   Acad Talk

4. SEM-4
   Acad Talk
   • Introduction to Deep Learning by AV
   • CST Lab Guide
5. SEM-5
   Acad Talk
   • Verilog Lab Guide
6. SEM-6
   Acad Talk
   • LTSpice Lab Guide

7. SEM-7
   Acad Talk

8. SEM-8
   Acad Talk