Machine Learning

• How to learn Deep Learning

  • ML is a set of tools that come from AI — and DL is a particular subset of ML

  • Don’t fret → Top-down approach

    • General roadmap

      • Get a good grasp of linear algebra and probability theory

      • Study all classic ML concepts

      • Learn how to implement those algorithms

      • Learn how to cook datasets, extract features, fine-tune parameters, and develop an intuition on which particular algorithm suits the task at hand

      • Get familiar with DL frameworks/libraries (PyTorch, TensorFlow, and Keras)

    • Start with pre-canned abstractions

    • Dig deeper only after some hands-on practice

  • Find a partner

  • Avoid cognitive overload

    • Bite precisely as much as you can chew

  • Set your sights

    • Work with something you can relate to will keep you motivated

    • Dive into subfield and their applications

      • Computer Vision

      • Natural Language Processing

      • Reinforcement Learning

  • Be competitive

    • Kaggle

  • Stay in the loop

    • Meetups

      • r/MachineLearning

      • Meetup.com

    • Conferences

      • ICLR

      • CVPR

      • NIPS

  • Use your programming chops

    • Academics’ codes are not the best

    • Use your programming skills to your advantage

      • Make good reusable libraries out of research code and notebooks

  • Brush up your math

    • Required to stay on the cutting edge and follow academic publications

  • Recommended resources

    • Deep Learning

      • Everything you need to get up to speed with some formal math

      • Part 1 (Linear Algebra, Probability and Information Theory, Numerical Computation, Machine Learning Basics) is the bare minimum introductory

    • Fast.ai

    • Deep Learning Specialization [5 courses] (DeepLearning.AI) | Coursera

      • Only prerequisite is knowing how to program

• Top-down, NLP-oriented roadmap

  • Mathematics

  • Tools

    • Python

    • PyTorch

  • Machine Learning

    • Write from Scratch

    • Compete

    • Do side projects

    • Deploy them

    • Supplementary

  • Deep Learning

    • Fast.ai

    • Do more competitions

    • Implement papers

    • Computer Vision

    • NLP

  • Large Language Models

    • Watch Neural Networks: Zero to Hero

    • Free LLM boot camp

    • Build with LLMs

    • Participate in hackathons

    • Read papers

    • Write Transformers from scratch.

    • Some good blogs

    • Watch Umar Jamil

    • Learn how to run open-source models.

    • Prompt Engineering

    • Fine-tuning LLMs

    • RAG

  • How to stay updated

  • Other curriculums/listicles you may find useful

• Build Better ML Systems Blog Series