Nesta página há links de interesse sobre Inteligência Artificial (IA), Machine Learning (ML), Tiny ML, Lógicas não clássicas e correlatos.

Sobre Soluções e Aplicações

Plataforma de Machine Learning (ML) Edge Impulse: https://edgeimpulse.com/

TensorFlow: https://www.tensorflow.org/

TensorFlow Lite: https://www.tensorflow.org/lite/microcontrollers?hl=pt-br

Nvidia Developer: https://developer.nvidia.com/deep-learning

Free Nvidia Training: https://www.nvidia.com/en-us/training/online/

List of Image Annotation Tools: https://www.v7labs.com/blog/best-image-annotation-tools

Ranking Free Image Annotation Tools: https://smartone.ai/blog/top-10-open-source-data-labeling-tools-for-computer-vision/

Sobre Machine Learning (ML)

Tiny ML Education (Harvard University): https://tinyml.seas.harvard.edu/

List of DataSets for ML Research: https://en.wikipedia.org/wiki/List_of_datasets_for_machine-learning_research

Sobre Image Annotation for Computer Vision

Guia de Marcação de Dados Visuais para ML: https://www.cloudfactory.com/image-annotation-guide

Sobre Artificial Neural Networks (ANN)

Neural Networks and Deep Learning: https://neuralnetworksanddeeplearning.com/

Neural Network Playground: https://www.tensorflow.org/

Neural Network Essentials: https://www.v7labs.com/blog/neural-network-architectures-guide

Sobre Convolutional Neural Networks (CNN)

Introduction to CNN: https://www.datacamp.com/tutorial/introduction-to-convolutional-neural-networks-cnns

Introduction to CNN (2): https://www.geeksforgeeks.org/introduction-convolution-neural-network/

Tutorial Treinamento de CNN: https://www.tensorflow.org/tutorials/images/cnn?hl=pt-br

Sobre Graph Neural Networks (GNN)

Compreendendo Convoluções em Gráficos: https://distill.pub/2021/understanding-gnns/

Introdução à GNN: https://distill.pub/2021/understanding-gnns/

Sobre Paraconsistent Logic (PL)

Fundamentos: https://plato.stanford.edu/entries/logic-paraconsistent/

Aplicações: https://sites.google.com/view/prof-arnaldo/pal2v-key-points

Sobre Lógica Difuza (Fuzzy)

Fundamentos: https://plato.stanford.edu/entries/logic-fuzzy/

Aplicações: http://fuzzywebintelligence.org/

FREE EBOOKS

Hagan, M. T.; Howard, B. D. Neural Network Design, Marting Hagan, 2º Edition, 1012 p., 2002. ISBN-13: ‎ 978-0971732117. Disponível em: https://hagan.okstate.edu/NNDesign.pdf

Rosa, J. L. G. R. Artificial Neural Networks – Models and Applications, In-Tech, 416 p., 2016. ISBN-13: 978-9535127055. Disponível em: https://mts.intechopen.com/storage/books/5191/authors_book/authors_book.pdf

Mehlig, B. Machine Learning with Neural Networks, University of Gothenburg, Sweden, 241 p., 2021. Disponível em: https://arxiv.org/pdf/1901.05639.pdf

Mohri, M; Rostamizadeh, A; Talwalkar, A. Foundations of Machine Learning, MIT Press, 2º edition, 505 p., 2018. learning. ISBN: 9780262039406. Disponível em: https://www.dropbox.com/s/38p0j6ds5q9c8oe/10290.pdf?dl=1

Goodfellow, I; Bengio, Y; Courville, A. Deep Learning. MIT Press, 2016. Disponível online: http://www.deeplearningbook.org

Halterman, R. L. Fundamentals of Python Programming, Southern Adventist University, 669 p., 2018. Draft: Disponível em: https://folk.ntnu.no/sverrsti/INGG1001-H2019/pythonbook_20191015.pdf

Lee, K. D. Python Programming Fundamentals, 2º edition, Springer, 241 p., 2014. ISBN: 978-1-4471-6642-9. Disponível em: https://aulavirtual.fio.unam.edu.ar/pluginfile.php/149985/mod_resource/content/1/2014_Book_PythonProgrammingFundamentals.pdf