This guide has tons of tricks that are usually not covered in textbooks: https://github.com/google-research/tuning_playbook
Uncategorized
(Very) Large Language Models in 2022
I feel quite amazed by the few-shot or even zero-shot learning capabilities of some recent (very) large language models. Here are three papers I read recently and would like to recommend:
– 540B PaLM by Google: https://arxiv.org/abs/2204.02311
– 11B Atlas by Meta: https://arxiv.org/abs/2208.03299
– 20B AlexaTM by Amazon: https://arxiv.org/abs/2208.01448
Image Classification Papers
Preparation: CNN course by Andrew Ng
Convolutional Neural Networks | Coursera
ImageNet Leaderboard (read in reverse order)
To Review Deep Learning
I will go back to work on deep learning after writing bash for 6 months. Here is my plan to pick up deep learning.
Foundation:
- Deep learning (Andrew Ng): https://www.coursera.org/specializations/deep-learning
- Book (Part 2): https://www.deeplearningbook.org/
CV or NLP:
- Convolutional Neural Networks for Visual Recognition (Spring 2017): https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&disable_polymer=true
- CS224N: Natural Language Processing with Deep Learning | Winter 2019: https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z&disable_polymer=true
PyTorch and Tensorflow:
- Fast AI (PyTorch): https://www.youtube.com/playlist?list=PLfYUBJiXbdtSIJb-Qd3pw0cqCbkGeS0xn&disable_polymer=true
- Tensorflow: https://www.coursera.org/specializations/tensorflow-in-practice