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The final project will involve training a complex recurrent neural network and applying it to a large scale NLP problem. Stanford’s Deep Learning for NLP course provides a deep excursion into cutting-edge research in deep learning applied to NLP.

Stanford Deep Learning for Natural Language Processing.

The MS Computational Network Toolkit is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. Stanford massive open online course covering:

Useful existing software for Machine Learning.Basic Math and TCS for Machine Learning.Various video lectures with syncronized slides that some people might be interested in. YouTube channel with podcast for papers.SurgeAI provides high-quality labelled data in more than 35 languages, (09/2022). Introduction: article 1, article 2, paper. Using LLM.int8(), a two-part quantization procedure, we can significantly reduce hardware requirements and still preserve performance levels for large language models. W&B is a platform for experiment tracking, hyperparameter tuning, model versioning and visualization. See the composer library, which has a functional API that allows you to load from Hugging Face. Some have accuracy-time tradeoff while others are speedups without performance degradation. Mosaic ML is a PyTorch library for adding algorithmic improvements to your models for free.Each week at our computational linguistics discussion group, Clippers, we spend the first few minutes discussing interesting, time-saving, or just plain nifty tools that we’ve found.