Excelentes vídeos explicando os conceitos de redes neurais:

https://www.3blue1brown.com/topics/neural-networks

Livro online:

https://geostatsguy.github.io/MachineLearningDemos_Book/intro.html

Livro gratuito:

https://arxiv.org/abs/2106.10165

Curso online:

https://github.com/microsoft/ai-agents-for-beginners/blob/main/translations/br/README.md

Erros comuns na análise estatística. Importante conhecer para evitar.

Common Statistical Pitfalls in Basic Science Research
Journal of the American Heart Association

Important paper: a comprehensive overview of the most common loss functions and metrics used across many different types of deep learning tasks, from general tasks such as regression and classification to more specific tasks in Computer Vision and Natural Language Processing.

Loss Functions and Metrics in Deep Learning
https://arxiv.org/abs/2307.02694

📚 𝟓 𝐥𝐢𝐯𝐫𝐨𝐬 𝐠𝐫𝐚𝐭𝐮𝐢𝐭𝐨𝐬 𝐬𝐨𝐛𝐫𝐞 𝐀𝐩𝐫𝐞𝐧𝐝𝐢𝐳𝐚𝐝𝐨 𝐝𝐞 𝐌á𝐪𝐮𝐢𝐧𝐚 𝐪𝐮𝐞 𝐫𝐞𝐜𝐨𝐦𝐞𝐧𝐝𝐨.
🇬🇧 𝟓 𝐟𝐫𝐞𝐞 𝐛𝐨𝐨𝐤𝐬 𝐨𝐧 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐈 𝐝𝐨 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝.

📗 𝚂𝚙𝚎𝚎𝚌𝚑 𝚊𝚗𝚍 𝙻𝚊𝚗𝚐𝚞𝚊𝚐𝚎 𝙿𝚛𝚘𝚌𝚎𝚜𝚜𝚒𝚗𝚐: 𝙰𝚗 𝙸𝚗𝚝𝚛𝚘𝚍𝚞𝚌𝚝𝚒𝚘𝚗 𝚝𝚘 𝙽𝚊𝚝𝚞𝚛𝚊𝚕 𝙻𝚊𝚗𝚐𝚞𝚊𝚐𝚎 𝙿𝚛𝚘𝚌𝚎𝚜𝚜𝚒𝚗𝚐, 𝙲𝚘𝚖𝚙𝚞𝚝𝚊𝚝𝚒𝚘𝚗𝚊𝚕 𝙻𝚒𝚗𝚐𝚞𝚒𝚜𝚝𝚒𝚌𝚜, 𝚊𝚗𝚍 𝚂𝚙𝚎𝚎𝚌𝚑 𝚁𝚎𝚌𝚘𝚐𝚗𝚒𝚝𝚒𝚘𝚗, by Jurafsky and Martin
https://web.stanford.edu/~jurafsky/slp3/ed3book_Jan25.pdf

📘 𝙼𝚊𝚌𝚑𝚒𝚗𝚎 𝙻𝚎𝚊𝚛𝚗𝚒𝚗𝚐 - 𝙰 𝙵𝚒𝚛𝚜𝚝 𝙲𝚘𝚞𝚛𝚜𝚎 𝚏𝚘𝚛 𝙴𝚗𝚐𝚒𝚗𝚎𝚎𝚛𝚜 𝚊𝚗𝚍 𝚂𝚌𝚒𝚎𝚗𝚝𝚒𝚜𝚝𝚜, by Wahlström et al.
https://smlbook.org/

📕 𝙰𝚗 𝙸𝚗𝚝𝚛𝚘𝚍𝚞𝚌𝚝𝚒𝚘𝚗 𝚝𝚘 𝚂𝚝𝚊𝚝𝚒𝚜𝚝𝚒𝚌𝚊𝚕 𝙻𝚎𝚊𝚛𝚗𝚒𝚗𝚐 (𝚁 𝚊𝚗𝚍 𝙿𝚢𝚝𝚑𝚘𝚗), by Garret et al.
https://www.statlearning.com/

📙 𝚄𝚗𝚍𝚎𝚛𝚜𝚝𝚊𝚗𝚍𝚒𝚗𝚐 𝙳𝚎𝚎𝚙 𝙻𝚎𝚊𝚛𝚗𝚒𝚗𝚐, by Simon J. D. Prince
https://udlbook.github.io/udlbook/

📗 𝙿𝚊𝚝𝚝𝚎𝚛𝚗 𝚁𝚎𝚌𝚘𝚐𝚗𝚒𝚝𝚒𝚘𝚗 𝚊𝚗𝚍 𝙼𝚊𝚌𝚑𝚒𝚗𝚎 𝙻𝚎𝚊𝚛𝚗𝚒𝚗𝚐, by Christopher M. Bishop
https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf

Livro em pdf:

Introduction to Machine Learning

https://arxiv.org/abs/2409.02668

Novo texto no Medium:

🇧🇷Informação: da arte à entropia
Uma breve introdução à teoria da informação de Shannon
https://francisco-rodrigues.medium.com/informa%C3%A7%C3%A3o-da-arte-%C3%A0-entropia-3c4344cafa0a

🇬🇧Information: from art to entropy
A brief introduction to Shannon’s information theory
https://francisco-rodrigues.medium.com/information-from-art-to-entropy-381871acf91f

Livro em pdf. Resumido, mas muito bom.

🇬🇧Book in pdf.

https://carl-mcbride-ellis.github.io/TOBoML/TOBoML.pdf

Tutorial sobre Manifold learning:

Manifold learning: what, how, and why
https://arxiv.org/abs/2311.03757

Curso gratuito:

https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-FX-15+V1&ncid=pa-so-link-138941-vt33

Excelente livro de Álgebra Linear em pdf:

Linear Algebra Done Right, Sheldon Axler
https://linear.axler.net/LADR4e.pdf

USP oferece curso online gratuito sobre aprendizado por reforço

https://saocarlos.usp.br/usp-oferece-curso-online-gratuito-sobre-aprendizado-por-reforco/

Notas de aula do curso de Aprendizado de Máquina de Andrew Ng e Tengyu Ma.

https://cs229-stanford-edu.translate.goog/?_x_tr_sl=en&_x_tr_tl=pt&_x_tr_hl=pt&_x_tr_pto=tc

Websites para baixar teses de doutorado. Pode ser útil para encontrar conteúdos novos e tutoriais:

FREE Websites for Downloading PhD Thesis

1. Open Access Theses and Dissertations

https://oatd.org/

2. Open Thesis

http://www.openthesis.org/

3. DART-Europe E-theses Portal

https://lnkd.in/d4SUFCTX

4. ProQuest Dissertations & Theses

https://www.proquest.com/

5. MIT Theses

https://lnkd.in/dMs-PuYs

6. Digital Library of Theses & Dissertations

http://www.ndltd.org/

7. Caltech Thesis

https://lnkd.in/dZ6f8T5J

8. British Library

https://www.bl.uk/

9. Electronic Theses & Dissertation Centre

https://etd.ohiolink.edu/

10. Harvard DASH

https://dash.harvard.edu/

Top 50 Large Language Model (LLM) Interview Questions

Livro em pdf: Statistical Methods for Machine Learning (Larry Wasserman).

🚨 Inscrições abertas para a terceira edição do curso "𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗼𝘀 𝗱𝗲 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝗱𝗮𝗱𝗲 𝗲 𝗘𝘀𝘁𝗮𝘁í𝘀𝘁𝗶𝗰𝗮 𝗽𝗮𝗿𝗮 𝗖𝗶ê𝗻𝗰𝗶𝗮 𝗱𝗲 𝗗𝗮𝗱𝗼𝘀".

🗓 O curso será de 10/11/2025 até 19/12/2025

📌 Para garantir a vaga, é preciso:

✅ 1 - Se inscrever no sistema Apolo:
https://uspdigital.usp.br/apolo/apoObterCurso?cod_curso=550400569&cod_edicao=25003&numseqofeedi=1

✅ 2 - Efetuar o pagamento no site da FAFQ (Valor total do curso: R$ 650,00)
https://www.fafq.org.br/curso/fundamentos-de-probabilidade-e-estatistica-para-ciencia-de-dados-icmc-usp/

🚨 𝗖𝗮𝘀𝗼 𝘁𝗲𝗻𝗵𝗮 𝗶𝗻𝘁𝗲𝗿𝗲𝘀𝘀𝗲 𝗲𝗺 𝗰𝗼𝗻𝗰𝗼𝗿𝗿𝗲𝗿 𝗮 𝗯𝗼𝗹𝘀𝗮, 𝗻ã𝗼 𝗲 𝗻𝗲𝗰𝗲𝘀𝘀𝗮𝗿𝗶𝗼 𝗲𝗳𝗲𝘁𝘂𝗮𝗿 𝗼 𝗽𝗮𝗴𝗮𝗺𝗲𝗻𝘁𝗼, sendo necessário apenas enviar os documentos comprobatórios de renda no email recebido após a inscrição.
➡️ Enviar até 30/09. A seleção será divulgada na primeira semana de outubro.

Essa é a última turma de 2025.

❗️ 𝗩𝗮𝗴𝗮𝘀 𝗹𝗶𝗺𝗶𝘁𝗮𝗱𝗮𝘀 ❗️

Playlist de Deep Learning:
https://www.youtube.com/playlist?list=PLgPbN3w-ia_PeT1_c5jiLW3RJdR7853b9

Livro em pdf disponível.

https://www.mlsysbook.ai/assets/downloads/Machine-Learning-Systems.pdf