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Starting point of learning tips to help others coming up to speed
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# Learning Tips | ||
Links and thoughts for helping to get up to speed | ||
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# ML/AI Courses | ||
* https://course.fast.ai/ | ||
* If you are limited in time, [Fast.ai](https://course.fast.ai/) is most efficient way to skill up in domain | ||
* [DeepLearning.ai](https://www.deeplearning.ai/) - Great resources to probe a little deeper into the theory, math, foundation for ML/NLP | ||
* https://www.deeplearning.ai/courses/machine-learning-specialization/ | ||
* https://www.deeplearning.ai/courses/deep-learning-specialization/ | ||
* https://www.deeplearning.ai/courses/natural-language-processing-specialization/ | ||
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# LLM Technical Background | ||
* Transformer | ||
* 9 min overview video: [Transformers, explained: Understand the model behind GPT, BERT, and T5](https://www.youtube.com/watch?v=SZorAJ4I-sA) | ||
* [Related Blog Post](https://daleonai.com/transformers-explained) | ||
* Paper: [Attention Is All You Need](https://arxiv.org/abs/1706.03762) | ||
* Blog: [Transformer: A Novel Neural Network Architecture for Language Understanding](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) | ||
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* Encoder/Decoders | ||
* Embeddings | ||
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# LLM General | ||
* Prompt Engineering: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/ | ||
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# LangChain | ||
* Course: https://learn.deeplearning.ai/langchain/lesson/1/introduction | ||
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# Newsletters | ||
Below are resources to follow for on-going developments in the AI community | ||
* ["The Batch"](https://www.deeplearning.ai/the-batch/) - Newsletter from DeepLearning.ai | ||
* https://synthetic.work/ | ||
* "Synthetic Work is a non-technical, free, weekly newsletter written by a former Fortune 1000 tech executive and Gartner analyst to understand how AI is changing our jobs and the way we work." |