Como construir um Generatively Pretrained Transformer (GPT):
https://www.youtube.com/watch?v=kCc8FmEb1nY&ab_channel=AndrejKarpathy
Material no Colab:
https://colab.research.google.com/drive/1JMLa53HDuA-i7ZBmqV7ZnA3c_fvtXnx-?usp=sharing
https://www.youtube.com/watch?v=kCc8FmEb1nY&ab_channel=AndrejKarpathy
Material no Colab:
https://colab.research.google.com/drive/1JMLa53HDuA-i7ZBmqV7ZnA3c_fvtXnx-?usp=sharing
YouTube
Let's build GPT: from scratch, in code, spelled out.
We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write…
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Livro em pdf: https://link.springer.com/book/10.1007/978-3-030-03499-3
SpringerLink
Understanding Statistics and Experimental Design
This open access textbook teaches essential principles that can help all readers generate statistics and correctly interpret the data. It offers a valuable guide for students of bioengineering, biology, psychology and medicine, and notably also for interested…
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Curso gratuito na USP:
550400544 - Professoras Programadoras: Introdução ao Pensamento Computacional com Scratch (100% online)
https://uspdigital.usp.br/apolo/apoObterCurso?cod_curso=550400544&cod_edicao=22001&numseqofeedi=1
550400544 - Professoras Programadoras: Introdução ao Pensamento Computacional com Scratch (100% online)
https://uspdigital.usp.br/apolo/apoObterCurso?cod_curso=550400544&cod_edicao=22001&numseqofeedi=1
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1674252987643.pdf
2.1 MB
A Brief Introduction to Machine
Learning for Engineers
Learning for Engineers
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MIT Introduction to Deep Learning http://introtodeeplearning.com/
MIT Deep Learning 6.S191
MIT's introductory course on deep learning methods and applications
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Excelente livro sobre deep learning (pdf online):
Understanding Deep Learning
https://udlbook.github.io/udlbook/
Understanding Deep Learning
https://udlbook.github.io/udlbook/
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Nonparametric Statistical Significance Tests in Python
https://jmyao17.github.io/Statistics/Nonparametric_Statistical_Significance_Tests.html
https://jmyao17.github.io/Statistics/Nonparametric_Statistical_Significance_Tests.html
Mais um curso de Álgebra Linear avançada:
https://www.youtube.com/playlist?list=PLOAf1ViVP13jdhvy-wVS7aR02xnDxueuL
https://www.youtube.com/playlist?list=PLOAf1ViVP13jdhvy-wVS7aR02xnDxueuL
Curso completo: Deep Reinforcement Learning
https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc
O método da máxima verossimilhança é um dos mais importantes em inferência estatística. Esse artigo aborda esse método e sua história. Para quem está interessando em aprender os fundamentos de como inferir os parâmetros de uma população, sugiro a leitura:
The Epic Story of Maximum Likelihood
https://twitter.com/docmilanfar/status/1620292922313945089
The Epic Story of Maximum Likelihood
https://twitter.com/docmilanfar/status/1620292922313945089
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