How to avoid machine learning pitfalls: a guide for academic researchers A really nice guide on doing machine learning the right way. Covers nearly all machine learning stages(before/after modeling, evaluation,…) and do's & don'ts in each stage. https://
arxiv.org/abs/2108.02497
@jeande_d
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Guide to Avoiding Machine Learning Pitfalls for Researchers
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Modular Deep Learning: Architectures, Scaling, and Causal Inference
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Modular Deep Learning A brief overview of modular deep learning. Covers modular architectures, scaling language models, causal inference and discovery, hierarchical RL, etc… Paper: https://
arxiv.org/abs/2302.11529
Blog: https://
ruder.io/modular-deep-l
earning/
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Website: https://
modulardeeplearning.com/?ref=ruder-io -
Chain of Thought Papers: Collection of LLM Reasoning Techniques
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Chain of Thought Papers A fantastic collection of papers on chain of thought(CoT). CoT is a method used for getting the most of LLMs where we prompt them with series of reasoning steps. We have seen numerous examples that shows that CoT actually works. https://
github.com/Timothyxxx/Cha
in-of-ThoughtsPapers
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Deep Learning Career Consideration and Recommendation
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Depends. For someone who want to do more of deep learning, probably yes.
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Deep Learning Course: Lectures and Resources from University of Liège
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Deep Learning , University of Liège A nice collection of lectures on various deep learning topics such as deep learning foundations, training neural networks, architectures, etc… Lectures: https://
youtube.com/playlist?list=
PLLqXZ_E-UXlyGPyiWStnC_Y0iizSv5jsG
… GitHub: https://
github.com/glouppe/info80
10-deep-learning
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Foundation Models Taxonomy and Evolution Paper Review
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The paper provides a nice taxonomy and evolution of foundational models. (foundation models: large models pre-trained on large datasets which can perform downstream tasks via ordinary transfer learning or in-context learning). https://
arxiv.org/abs/2302.09419 -
Comprehensive Survey on Pretrained Foundation Models from BERT to ChatGPT
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A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT A nice review of recent advances, current and future researches in pretrained foundation models in NLP, computer vision, graph learning, and other modalities. https://
arxiv.org/abs/2302.09419 -
Data-Centric AI: Shifting Focus from Models to Datasets
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AI systems involves data & model. Conventional machine learning focus on building better models. Data-Centric AI focus on curating datasets. Data-centric approach is an emerging field. So nice to see schools and orgs releasing course on this topic! https://
dcai.csail.mit.edu -
MIT 2023 Introduction to Data-Centric AI Course
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[NEW COURSE] Introduction to Data-Centric AI – MIT 2023 A new course that covers many techniques related to data-centric approach such as data labelling, data curation, class imbalance, interpretability, data augmentation, prompt engineering… Lectures: https://
youtube.com/playlist?list=
PLnSYPjg2dHQKdig0vVbN-ZnEU0yNJ1mo5
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Zero-shot Text Classification Models and APIs for NLP Tasks
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Take a look at Hugging Face zero-shot text classification page, many models there: https://
huggingface.co/tasks/zero-sho
t-classification
… You can also try APIs(Open AI, Cohere, etc…). Easier option maybe.