CODEPROGRAMMER Telegram 2919
๐Ÿ“Œ Big road map from William Brown: how and what to study for development in the field of generative neural networks and AI

Here are tons of useful links for each section, some of these links have already been posted in the channel, for example, links to incredibly useful tutorials from Lilian Weng .
Here are the main sections that this roadmap covers:
โœ”๏ธ time series analysis, Markov models
โœ”๏ธ recurrent neural networks, LSTM and GRU,
โœ”๏ธ working with language: tokenization, etc.
โœ”๏ธ fine tuning methods for LLM
โœ”๏ธ LLM assessment and benchmarks
โœ”๏ธ LLM optimization: quantization
โœ”๏ธ context scaling
โœ”๏ธ GAN, diffusion models
โœ”๏ธ multimodal models

โ˜„๏ธ Roadmap ๐Ÿ˜ฎ

#python #deeplearning #ML

๐ŸŒ http://www.tgoop.com/codeprogrammer โœ…๏ธ
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๐Ÿ“Œ Big road map from William Brown: how and what to study for development in the field of generative neural networks and AI

Here are tons of useful links for each section, some of these links have already been posted in the channel, for example, links to incredibly useful tutorials from Lilian Weng .
Here are the main sections that this roadmap covers:
โœ”๏ธ time series analysis, Markov models
โœ”๏ธ recurrent neural networks, LSTM and GRU,
โœ”๏ธ working with language: tokenization, etc.
โœ”๏ธ fine tuning methods for LLM
โœ”๏ธ LLM assessment and benchmarks
โœ”๏ธ LLM optimization: quantization
โœ”๏ธ context scaling
โœ”๏ธ GAN, diffusion models
โœ”๏ธ multimodal models

โ˜„๏ธ Roadmap ๐Ÿ˜ฎ

#python #deeplearning #ML

๐ŸŒ http://www.tgoop.com/codeprogrammer โœ…๏ธ

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