Overview

This repository contains my artificial intelligence learning journey (mainly ML & DL).

All the resources mentioned in the repository are ones that I genuinely used & found them to be good quality.

Machine learning

Fundamentals

• Gradient descent:

  • https://www.youtube.com/watch?v=IHZwWFHWa-w
  • https://www.youtube.com/watch?v=sDv4f4s2SB8
  • https://www.youtube.com/watch?v=qg4PchTECck

• Optimizers:

  • Adam Optimizer explained (Momentum, Adagrad, Adam): https://www.youtube.com/watch?v=IWvTU6swl_E
  • Momentum, Adagrad, RMS Prob & Adam explained: https://www.youtube.com/watch?v=MD2fYip6QsQ
  • 16 most popular optimizers (Refresher / Covers AdamW & Yogi too): https://youtu.be/7m8f0hP8Fzo

• Bias & Variance:

  • watch the first 3 videos from this playlist: https://www.youtube.com/playlist?list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc

• Regularization:

  • intuition / simply explained: https://www.youtube.com/watch?v=EehRcPo1M-Q
  • Bias and Variance (to understand the need for regularization):
    • watch the first 3 videos from this playlist: https://www.youtube.com/playlist?list=PLkDaE6sCZn6Hn0vK8co82zjQtt3T2Nkqc
  • L1 & L2 explained: https://youtu.be/rQVODj9YDp0
  • L1 & L1 (mathematical): https://www.youtube.com/watch?v=6g0t3Phly2M
  • Dropout explanation: https://youtu.be/D8PJAL-MZv8
  • Why dropout works: https://youtu.be/ARq74QuavAo
  • More dropout explanation: https://youtu.be/kry2JghtMSY

• Feature Scaling / Batch normalization:

  • normalization & standardization explained:
    • https://www.youtube.com/watch?v=sxEqtjLC0aM
    • https://www.youtube.com/watch?v=W2vqsTg-rDU
    • https://www.youtube.com/watch?v=bqhQ2LWBheQ
  • Batch normalization:
    • https://www.youtube.com/watch?v=yXOMHOpbon8
    • https://www.youtube.com/watch?v=dXB-KQYkzNU
    • https://www.youtube.com/watch?v=DtEq44FTPM4

Algorithms / Models

• Linear regression

  • https://medium.com/@pritioli/implementing-linear-regression-from-scratch-747343634494
  • https://www.youtube.com/watch?v=VmbA0pi2cRQ

• logistic regression

  • understanding logistic regression (lecture): https://www.youtube.com/watch?v=abB3fwfPy14
  • logistic regression theory & implementation: https://www.youtube.com/watch?v=S6iuhdYsGC8

ML Libraries

• matplotlib

  • Tutorial for learning the basics: https://matplotlib.org/stable/tutorials/pyplot.html
  • Read lifecycle of a plot: https://matplotlib.org/stable/tutorials/lifecycle.html
  • Randomly read some parts from user guide (depends on your curiosity & what you want to learn)
  • Read Quick start guide page to have an overview of how matplotlib works
  • Read Axes introduction for an overview of how Axes work

• Pandas

  • https://www.youtube.com/watch?v=tRKeLrwfUgU
  • https://pandas.pydata.org/docs/user_guide/10min.html
  • https://www.youtube.com/watch?v=EhYC02PD_gc

Deep learning

Fundamentals

• pytorch

  • pytorch crash course: https://www.youtube.com/watch?v=c36lUUr864M
  • pytorch autograd explained: https://www.youtube.com/watch?v=hjnVLfvhN0Q
  • 24h course: https://www.youtube.com/watch?v=Z_ikDlimN6A&t
  • This is my goto resource for anything related to basics in pytorch: https://www.learnpytorch.io/

• Computer Vision

  • image processing concepts: https://www.youtube.com/playlist?list=PLcQ8H2FtZMugV6cbWfdaIgbkGZjKgOYAx
  • opencv crash course: https://www.youtube.com/watch?v=oXlwWbU8l2o
  • computer vision in pytorch (course): https://www.youtube.com/watch?v=Z_ikDlimN6A&t=50420s (CV section)

Mathematics

Statistics

• Variability (range, standard deviation & variance): https://www.youtube.com/watch?v=s7WTQ0H0Acc