How I am learning Artificial Intelligence in 2024

• Study Natural Language Processing
• Apply knowledge to projects

Improvements from '2023 Learning Experience'

Details: last year's Computer Vision Things I am improving

• Focus on high level libraries like transformers and diffusers over pytorch
  • Advantage: Few lines to have end to end pipeline
  • Advantage: Getting to solution quickly instead of 2 weeks of implementing from scratch
• Do more Kaggle competitions compared to courses. (10 courses & 3 competitions last year, Aiming for 10+ competitions this year)
• Continue coding in pytorch, build code cookbook for experimentation
• Visualize model internals to understand it better. (Didn't do this part last year)
• Understand maths aspect of neural networks. (Didn't do this part last year.)

NLP - NLP Landscape's two entry paths

• NLP Landscape has two entry points, one slow, long & easier path & other short but steep path
  • Long & Slow path: DL Basics -> Simple NN -> CNN -> RNN -> LSTM -> Word2Vec -> Attention -> LLMs
  • Short & Steep path: DL Basics -> Attention is all you need -> LLMs
    • Everything in NLP & CV is building on top of this single paper. Highest citations, highest used architecture, is most varied kinds of problems.
    • Understand this thoroughly, because everything builds on this

Quarter 1 - Things Learned

NLP - 5 STAR RESOURCES

Type	Details	Progress
1: Course	Huggingface NLP
2: Kaggle Competition	Disaster Tweet Classification
3: Research Paper	Attention is all you need
4: Research Paper	One Model to learn them all
5: Youtube Video	3Blue1Brown: Attention in transformers, visually explained
6: Youtube Video	3Blue1Brown: But what is a GPT? Visual intro to transformers
7: Youtube Video	Campus X: Epic History of Large Language Models
8: Youtube Video	Campus X: Self Attention
9: Youtube Video	Campus X: Attention
10: Course	School of AI - ERA3

Computer Vision - 5 STAR RESOURCES

Type	Details	Progress
1: Course	Huggingface timm
2: Course	Huggingface diffusers
3: Course	Huggingface Community Vision Course
4: Course	Zero to Mastery Tensorflow

1.2. Kaggle Competitions - 3 Competitions

Competition	Progress
Cats vs Dogs - End to End Pipeline
10 Small Objects Recognition(CIFAR10)
Imagenet Classification

Corporate Trainings

1. Data Engineering for MTech Students for LTI Mindtree
2. Application Development for MTech Students for LTI Mindtree
3. How Developers can learn Artificial Intelligence - a DevRel Talk