Deep Narain Singh's starred repositories
system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
google-research
Google Research
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course: TensorFlow for Deep Learning Research.
machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
system_design
Preparation links and resources for system design questions
machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
awesome-multimodal-ml
Reading list for research topics in multimodal machine learning
100-nlp-papers
100 Must-Read NLP Papers
awesome-implicit-representations
A curated list of resources on implicit neural representations.
awesome-papers
Papers & presentation materials from Hugging Face's internal science day
ml-hypersim
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
robustness
Corruption and Perturbation Robustness (ICLR 2019)
acl2020-openqa-tutorial
ACL2020 Tutorial: Open-Domain Question Answering
speaker-id
This repository contains audio samples and supplementary materials accompanying publications by the "Speaker, Voice and Language" team at Google.
tldr-transformers
The "tl;dr" on a few notable transformer papers (pre-2022).
deeplearning.ai-GANs-Specialization
A Generative Adversarial Networks (GANs) Specialization made by deeplearning.ai on Coursera
keep-learning-ml
A club to keep learning about ML
CodeExamples
We have put together some examples of different well known machine learning algorithms. This is to make it easier to understand how it looks like when working with machine learning in code. Happy hacking!