- Understanding of the Neural Nets and Classical ML this includes Transformer Architecture, CNNs, LSTMs, UNets and SVM, XGBoost, (param selection, EDA) etc.
- Understanding of Maths: Statistics, Probability, Linear Algebra, little bit of calculus (little bit of everything)
- Implementaion of paper which I can train on my laptop :smaller versions of large models/ finetuning
- Python all the way down
- Implementation of Data Pipelines for both image and text
- Developer stuff: Docker, AWS and API building etc.
- Writing down stuff (maybe): Research findings as well as technical documentation
- Implementation of complex models from scratch and not just a toy version
- Train a large model on multi-GPU clusture, which involves not just the training but the entire jazz
- Valuable contribution to an opensource project
- Data scientist with capability to reason model performance and not just build, trial and error.
- Writing a clean and optimized code
- Being less pretentious, and writing more code than words :)