IA - ACADEMIA
Material para aprendizagem pessoal
Os conteúdos e recursos aqui apresentados têm caráter informativo, para servir de referência de estudo. O objetivo é contribuir para o desenvolvimento de conhecimentos, habilidades e competências pessoais nesta área. Os conteúdos de terceiros não são reprooduzidos, mas direcionados para as páginas dos autores ou distribuidores, não existindo qualquer vínculo legal, comercial ou acadêmico entre o portal iaml.tec.br e as instituições apontadas.
PUBLICAÇÕES, LIVROS e PERIÓDICOS
VÍDEOS
CURSOS
TEXTOS BÁSICOS E TUTORIAIS
INFOGRÁFICOS
ANIMAÇÕES, SLIDES, MODELOS, RECURSOS e SIMULADORES
DATA SCIENCE
ARTIGOS CIENTÍFICOS
PROGRAMAS DE PESQUISA, DESENVOLVIMENTO E INOVAÇÃO
ALIANÇAS E PARCERIAS
BLOGs
HISTÓRIA
GLOSSÁRIOS, FOLHAS DE REFERÊNCIAS E DEFINIÇÕES
PUBLICAÇÕES, LIVROS e PERIÓDICOS
free pdf preview - TinyML Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power MicrocontrollersTinyML .
pdf - IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. Conference proceedings ITEM 2020, IoT Streams 2020.
pdf - Free book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books..
pdf - The woefully complete guide by Alex Reinhart. Statistics Done Wrong is a guide to the most popular statistical errors and slip-ups committed by scientists every day, in the lab and in peer-reviewed journals. Many of the errors are prevalent in vast swaths of the published literature, casting doubt on the findings of thousands of papers. Statistics Done Wrong assumes no prior knowledge of statistics, so you can read it before your first statistics course or after thirty years of scientific practice..
pdf - The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it's a fast-paced introduction to the Python language aimed at researchers and scientists..
pdf - Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks , a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data; and Deep learning, a powerful set of techniques for learning in neural networks.
MAGAZINE - Wireless Quarter
Free web magazine from Nordic Semiconductor ₢ - Issue 2,2021
Special Report - Envolving Intelligence: WQ investigates the power of tiny machine learning and reveals how it will transform the IoT and drive a digital revolution.
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JOURNAL - Artificial Intelligence Research
Free web journal from IDTechEx covers the latest artificial intelligence (AI) research and industry news. Launched in March 2017, this free portal covers progress of AI in all its forms.
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JOURNAL - Quantum AiFuture
Frank Feather Editor
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Free web magazine from Nordic Semiconductor ₢ - Issue 2,2021
Special Report - Envolving Intelligence: WQ investigates the power of tiny machine learning and reveals how it will transform the IoT and drive a digital revolution. .
Free web journal from IDTechEx covers the latest artificial intelligence (AI) research and industry news. Launched in March 2017, this free portal covers progress of AI in all its forms. .
Frank Feather Editor .
VÍDEOS
Palestra TinyML - Aprendizado de Máquina Embarcado (Embedded Machine Learning: an introduction)
Palestra online proferida pelo Prof. Marcelo Rovai em 19/08/2021 e promovida pelo Grêmio Cecília Meireles do Instituto Federal de Educação, Ciência e Tecnologia de São Paulo - Campus Guarulhos. A apresentação bem didática inclui interessantes demonstrações de projetos de aplicação com TinyML nas áreas de som, visão e vibração.
Live ROS ML (ROS Developers LIVE Class #94: Basic Machine Learning for Robotics)
Basic concepts of machine learning, focusing on the linear regression algorithm.
By The Construct
tinyML Talks - Pete Warden: Getting started with TinyML.
pdf - Slides da apresentação.
Conclave - tinyML Summit 2021 tiny Talks.
Vídeo - Embarcando Deep Learning: Nano GPUs e TinyML - Marcelo Pias.
Vídeo - Introdução à Machine Learning, Deep Learning e Reinforcement Learning - OPENCADD.
Vídeo - Intro to TinyML Part 1: Training a Neural Network for Arduino in TensorFlow | Digi-Key Electronics.
Vídeo - Intro to TinyML Part 2: Deploying a TensorFlow Lite Model to Arduino | Digi-Key Electronics.
Vídeo - Why Choose Deep Learning? | Deep Learning for Engineers, Part 1
Mat Lab Tech Talks with Brian Douglas.
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Vídeo - Working with Synthetic Data | Deep Learning for Engineers, Part 2
Mat Lab Tech Talks with Brian Douglas.
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Vídeo - Data Preprocessing and the Short-Time Fourier Transform | Deep Learning for Engineers, Part 3
Mat Lab Tech Talks with Brian Douglas.
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Vídeo - Using Transfer Learning | Deep Learning for Engineers, Part 4
Mat Lab Tech Talks with Brian Douglas.
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Vídeo - Deploying Deep Learning Models | Deep Learning for Engineers, Part 5.
Vídeo - DARPA - AI COLLOQUIUM (MARCH 6-7, 2019) - AI Colloquium Session Videos.
Vídeo - GoogLeNet - Parte I: Fundamentos básicos.
Vídeo - GoogLeNet - Parte II: Detalhes da arquitetura.
Vídeo - GoogLeNet - Parte III: Comparação de resultados.
Vídeos - NVIDIA ON-DEMAND
Extensive catalog of sessions, podcasts, demos, research posters and more. Some content may require NVIDIA Developer Program membership.
Vídeos - Neural networks
Mas o que *é* uma Rede Neural? | Deep learning, capítulo 1
Descida de gradiente, como as redes neurais aprendem | Deep learning, capítulo 2
What is backpropagation really doing? | Chapter 3, Deep learning
Backpropagation calculus | Chapter 4, Deep learning
Vídeos - ENISA - Artificial Intelligence Threat Landscape
Palestra online proferida pelo Prof. Marcelo Rovai em 19/08/2021 e promovida pelo Grêmio Cecília Meireles do Instituto Federal de Educação, Ciência e Tecnologia de São Paulo - Campus Guarulhos. A apresentação bem didática inclui interessantes demonstrações de projetos de aplicação com TinyML nas áreas de som, visão e vibração.
Basic concepts of machine learning, focusing on the linear regression algorithm.
By The Construct
Mat Lab Tech Talks with Brian Douglas. .
Mat Lab Tech Talks with Brian Douglas. .
Mat Lab Tech Talks with Brian Douglas. .
Mat Lab Tech Talks with Brian Douglas. .
Extensive catalog of sessions, podcasts, demos, research posters and more. Some content may require NVIDIA Developer Program membership.
Mas o que *é* uma Rede Neural? | Deep learning, capítulo 1Descida de gradiente, como as redes neurais aprendem | Deep learning, capítulo 2What is backpropagation really doing? | Chapter 3, Deep learningBackpropagation calculus | Chapter 4, Deep learning DATA SCIENCE
Guide - A Beginner’s Guide to Regression Analysis in Machine Learning Regression analysis explained with examples, illustrations, animations and cheat sheets.
Guide - Comprehensive Guide To Regression For Dummies.
Guide - Beginners Guide To Logistic Regression In Python.
Guide - Beginners Guide To Linear Regression In Python.
Resumos - Cheat Sheets for Machine Learning Interview Topics.
Resumos - Cheat Sheets for Machine Learning and Data Science.
Animações - Machine Learning Animations.
Visualization is a great tool in understanding complex/new topics. This page contains animations for various Machine Learning topics aimed at creating a better understanding of the topics.
Visualization is a great tool in understanding complex/new topics. This page contains animations for various Machine Learning topics aimed at creating a better understanding of the topics.
INFOGRÁFICOS
INFOGRÁFICO - Artificial-Intelligence-and-Machine-Learning-Infographic Posted on December 6, 2016.
INFOGRÁFICO - Here is Everything You Need to Know About Machine Learning (Infographic) by Abhimanyu Sundar - October 10, 2019.
INFOGRÁFICO - Cartoon Infographic on Building the Machine Learning Model. *(Drawn by Chanin Nantasenamat) - January 1, 2020.
INFOGRÁFICO - DATA & AI LANDSCAPE 2020 - by Matt Turck - VC at FirstMark (https://mattturck.com/data2020/)
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INFOGRÁFICO - AI Use Cases - by oneRagtime
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INFOGRÁFICO - Overview of Machine Learning frameworks and libraries
By Nguyen, Giang; Dlugolinsky, Stefan et al | 2019.
INFOGRÁFICO - Data Science Tools
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By Nguyen, Giang; Dlugolinsky, Stefan et al | 2019.
ANIMAÇÕES, SLIDES, MODELOS, RECURSOS e SIMULADORES
Animações - Machine Learning Animations.
Visualization is a great tool in understanding complex/new topics. This page contains animations for various Machine Learning topics aimed at creating a better understanding of the topics.
Tool box - Deep Learning Toolbox.
Design, train, and analyze deep learning networks.
Tool box - ONNX - Open Neural Network Exchange.
Build and train a machine learning model to meet your project goals using the tools that best meet your needs. ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.
Model - Caffe Model Zoo - Deep learning framework.
These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
Model - Caffe Model Zoo - Github.
These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
Teaching Kits - NVIDIA DLI TEACHING KITS FOR EDUCATORS.
Universities are at the forefront of nurturing the next generation in the emerging technologies of accelerated computing, data science, and AI. NVIDIA Deep Learning Institute (DLI) Teaching Kits lower the barrier of incorporating AI and GPU computing in coursework with downloadable teaching materials and online courses that provide the foundation for understanding and building hands-on expertise in these critical areas.
Slides - Neural Network .
- Pereceptron - AND, OR, NOR Logic.
Kit - DeepRacer
O AWS DeepRacer oferece uma maneira interessante e divertida de dar os primeiros passos com o Reinforcement Learning (RL – Aprendizado por reforço). O RL é uma técnica avançada de Machine Learning (ML) que adota uma abordagem de treinamento de modelos muito diferente dos outros métodos de Machine Learning. Sua grande vantagem é que ele aprende comportamentos muito complexos sem exigir dados de treinamento rotulados e pode tomar decisões de curto prazo ao mesmo tempo que otimiza para uma meta de prazo mais longo.
Visualization is a great tool in understanding complex/new topics. This page contains animations for various Machine Learning topics aimed at creating a better understanding of the topics.
Design, train, and analyze deep learning networks.
Build and train a machine learning model to meet your project goals using the tools that best meet your needs. ONNX is an open format built to represent machine learning models. ONNX defines a common set of operators - the building blocks of machine learning and deep learning models - and a common file format to enable AI developers to use models with a variety of frameworks, tools, runtimes, and compilers.
These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
These models are learned and applied for problems ranging from simple regression, to large-scale visual classification, to Siamese networks for image similarity, to speech and robotics applications.
Universities are at the forefront of nurturing the next generation in the emerging technologies of accelerated computing, data science, and AI. NVIDIA Deep Learning Institute (DLI) Teaching Kits lower the barrier of incorporating AI and GPU computing in coursework with downloadable teaching materials and online courses that provide the foundation for understanding and building hands-on expertise in these critical areas.
- Pereceptron - AND, OR, NOR Logic.
O AWS DeepRacer oferece uma maneira interessante e divertida de dar os primeiros passos com o Reinforcement Learning (RL – Aprendizado por reforço). O RL é uma técnica avançada de Machine Learning (ML) que adota uma abordagem de treinamento de modelos muito diferente dos outros métodos de Machine Learning. Sua grande vantagem é que ele aprende comportamentos muito complexos sem exigir dados de treinamento rotulados e pode tomar decisões de curto prazo ao mesmo tempo que otimiza para uma meta de prazo mais longo.
PAPERS
Papers - Four Deep Learning Papers to Read in February 2021
By Robert Lange | towardsdatascience.com/ | Jan 30, 2021 | 5 min read | With listen .
Papers - DeepOcean: A General Deep Learning Framework for Spatio-Temporal Ocean Sensing Data Prediction.
By Robert Lange | towardsdatascience.com/ | Jan 30, 2021 | 5 min read | With listen .
ARTIGOS, APPLICATION NOTES e WHITE PAPERS
Artigo Como a Inteligência Artificial está reinventando o que os computadores são.
Por MIT Technology Review em 23 de novembro de 2021
As três maneiras principais pelas quais a Inteligência Artificial está mudando a computação.
Artigo Machine Learning: Algorithms, Real‑World Applications and Research Directions
By Nguyen, Giang; Dlugolinsky, Stefan et al | March 22, 2021
This study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
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Artigo Artificial intelligence in healthcare
Applications, risks, and ethical and societal impacts
By Karim Lekadir; Gianluca Quaglio, Catherine Gallin et al | June, 2022
This study provides an overview of AI health-related applications and an analysis of the potential of AI to transform the provision of healthcare. The study will also define, assess and clarify risks in the current and potential applications of AI in the domain of healthcare. At the same time, it will consider major clinical, socio-ethical and regulatory aspects of AI in its various health applications. Finally, the study also proposes a series of policy options aimed at minimising the risks of medical AI, enhancing governance at the EU level and strengthening its responsible development.
Por MIT Technology Review em 23 de novembro de 2021
As três maneiras principais pelas quais a Inteligência Artificial está mudando a computação.
By Nguyen, Giang; Dlugolinsky, Stefan et al | March 22, 2021
This study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
Applications, risks, and ethical and societal impacts
By Karim Lekadir; Gianluca Quaglio, Catherine Gallin et al | June, 2022
This study provides an overview of AI health-related applications and an analysis of the potential of AI to transform the provision of healthcare. The study will also define, assess and clarify risks in the current and potential applications of AI in the domain of healthcare. At the same time, it will consider major clinical, socio-ethical and regulatory aspects of AI in its various health applications. Finally, the study also proposes a series of policy options aimed at minimising the risks of medical AI, enhancing governance at the EU level and strengthening its responsible development.
CURSOS
Edge Impulse University - Teach edge machine learning using Edge Impulse
The Edge Impulse university program empowers educators from around the world to train the next generation of edge machine learning professionals. Program details coming soon (May 2022). Sign up for updates and be the first to know when applications open. .
CURSO - Machine Learning Crash Course with TensorFlow APIs
Google's fast-paced, practical introduction to machine learning.
A self-study guide for aspiring machine learning practitioners Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. .
CURSO - Artificial Intelligence Tutorial | Artificial Intelligence Full Course | AI Tutorial | Simplilearn.
CURSO - CS50's Introduction to Artificial Intelligence with Python. Learn to use machine learning in Python in this introductory course on artificial intelligence..
COURSE REVIEW - How to Break Into AI? Harvard CS50AI: Introduction to Artificial Intelligence Course Review.
CURSO - CS249r: Tiny Machine Learning Applied Machine Learning for Embedded IoT Devices.
CURSO - Deploying TinyML.
CURSO - LEARN OPENCV in 3 HOURS with Python | Including 3x Example Projects. Murtaza's Workshop - Robotics and AI - June 2020.
CURSO - OpenCV Course - Full Tutorial with Python. freeCodeCampOrg.
CURSO Practical Machine Learning with TensorFlow 2.0 - Dr.Ashish Tendulkar - Prof.B.Ravindran
Indian Institute of Technology Madras & Google
National Programme on Technology Enhanced Learning (NPTEL)
Livro: 
CURSO Deep Learning - Instituto de Informática da UFG.
CURSO NVIDIA DEEP LEARNING INSTITUTE: Education and Training Solutions to Solve the World’s Most Challenging Problems
CURSO - Mathematics of Big Data and Machine Learning
Author(s): Dr. Jeremy Kepner - Dr. Vijay Gadepally
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AULA - 1. Artificial Intelligence and Machine Learning (MIT Lincoln Laboratory)
Mathematics of Big Data and Machine Learning.
AULA - 2. Cyber Network Data Processing; AI Data Architecture (MIT Lincoln Laboratory)
Mathematics of Big Data and Machine Learning.
CURSO FAST.AI
Cursos online gratuitos e outros recursos.
Cursos: * Practical Deep Learning for Coders, * Part 2: Deep Learning from the Foundations, * Practical Data Ethics, * Computational Linear Algebra e * Code-First Introduction to Natural Language Processing.
CURSO ADVANCED NLP with spaCY
Cursos online gratuitos e outros recursos.
Cursos: * In this free and interactive online course you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches. It includes 55 exercises featuring videos, slide decks, multiple-choice questions and interactive coding pratice in the browser.
CURSO - IESTI01 - TinyML - Machine Learning for Embedding Devices - UNIFEI (2021.1)
Curso online ministrado no Instituto de Engenharia de Sistemas e Tecnologias da Informação – IESTI - Campus de Itajubá.
É um curso introdutório na interseção entre o Aprendizado de Máquina (Machine Learning) e dispositivos embarcados (Embedded Devices). A difusão de dispositivos embarcados com ultra-baixo consumo de energia (da ordem de miliwatts), juntamente com a introdução de frameworks de aprendizado de máquina dedicados a dispositivos embarcados, como TensorFlow Lite para Microcontroladores (TF Micro), permitirá a proliferação em massa de dispositivos IoT potencializados por IA (“AIoT”).
Professor Marcelo Rovai
AI - Part 1 - Basics
IESTI01 TinyML Class 1 - About the course and syllabus
IESTI01 TinyML Class 2 - Introduction to TinyML
IESTI01 TinyML Class 3 - TinyML challenges
IESTI01 TinyML Class 4 - Jupyter Notebook, Google CoLab and Python Review
IESTI01 TinyML Class 5 - The ML Paradigm
IESTI01 TinyML Class 6 - ML regression with DNN
IESTI01 TinyML Class 7 - The building blocks of ML -Part A
IESTI01 TinyML Class 8 - The building blocks of ML -Part B
IESTI01 TinyML Class 9 - The building blocks of ML -Part C
IESTI01 TinyML Class 10 - Introducing Convolutions
IESTI01 TinyML Class 11 - CNN Recap
IESTI01 TinyML Class 12 - Preventing Overfitting
AI - Part 2 - TinyML Applications
IESTI01 TinyML Class 13 - AI Lifecycle and ML Workflow
IESTI01 TinyML Class 14 - AI Lifecycle and ML Workflow
IESTI01 TinyML Class 15 - Introduction to Edge Pulse Studio
IESTI01 TinyML Class 16 - Gesture Classification - Edge Pulse Studio Project
IESTI01 TinyML Class 17 - Anomaly Detection with TinyML
IESTI01 TinyML Class 18 - Data Engineering for TinyML
IESTI01 TinyML Class 19 - TinyML Kit Overview, Installation and Test
IESTI01 TinyML Class 20 - Gesture Classification & Anomaly Detection with Arduino Nano-33 BLE
IESTI01 TinyML Class 21 - Keyword Spotting (KWS) Introduction
IESTI01 TinyML Class 22 - Keyword Spotting (KWS) - Edge Impulse Studio
IESTI01 TinyML Classes 23 and 24 - ML for the physical world
Guest Lecturer: Daniel Situnayake, Edge Impulse
IESTI01 TinyML Class 25 - Visual Wake Words - Introduction
IESTI01 TinyML Class 26 - VWW - Demo/Lab
IESTI01 TinyML Class 27 - Parte A - Image Classification using Edge Impulse Studio
IESTI01 TinyML Class 27 - Parte B - Arduino Code Walkthrough
IESTI01 TinyML Class 28 - VWW - Demo/Lab
IESTI01 TinyML Class 28 - "Privacy in Context" by Susan Kennedy (Harvard)
IESTI01 TinyML Classes 29 and 30 - Group Presentations
» Group 1 - Seismic Detection
» Group 2 - Fire Detection
» Group 3 - Covid Detection (cough)
» Group 4 - Mask Detection
» Group 5 - Personal Trainer
The Edge Impulse university program empowers educators from around the world to train the next generation of edge machine learning professionals. Program details coming soon (May 2022). Sign up for updates and be the first to know when applications open. .
Google's fast-paced, practical introduction to machine learning.
A self-study guide for aspiring machine learning practitioners Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises. .
Indian Institute of Technology Madras & Google
National Programme on Technology Enhanced Learning (NPTEL)
Author(s): Dr. Jeremy Kepner - Dr. Vijay Gadepally
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Mathematics of Big Data and Machine Learning.
Mathematics of Big Data and Machine Learning.
Cursos online gratuitos e outros recursos.
Cursos: * Practical Deep Learning for Coders, * Part 2: Deep Learning from the Foundations, * Practical Data Ethics, * Computational Linear Algebra e * Code-First Introduction to Natural Language Processing.
Cursos online gratuitos e outros recursos.
Cursos: * In this free and interactive online course you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches. It includes 55 exercises featuring videos, slide decks, multiple-choice questions and interactive coding pratice in the browser.
Curso online ministrado no Instituto de Engenharia de Sistemas e Tecnologias da Informação – IESTI - Campus de Itajubá.
É um curso introdutório na interseção entre o Aprendizado de Máquina (Machine Learning) e dispositivos embarcados (Embedded Devices). A difusão de dispositivos embarcados com ultra-baixo consumo de energia (da ordem de miliwatts), juntamente com a introdução de frameworks de aprendizado de máquina dedicados a dispositivos embarcados, como TensorFlow Lite para Microcontroladores (TF Micro), permitirá a proliferação em massa de dispositivos IoT potencializados por IA (“AIoT”).
Professor Marcelo Rovai
IESTI01 TinyML Class 1 - About the course and syllabusIESTI01 TinyML Class 2 - Introduction to TinyMLIESTI01 TinyML Class 3 - TinyML challengesIESTI01 TinyML Class 4 - Jupyter Notebook, Google CoLab and Python ReviewIESTI01 TinyML Class 5 - The ML ParadigmIESTI01 TinyML Class 6 - ML regression with DNNIESTI01 TinyML Class 7 - The building blocks of ML -Part A IESTI01 TinyML Class 8 - The building blocks of ML -Part B IESTI01 TinyML Class 9 - The building blocks of ML -Part C IESTI01 TinyML Class 10 - Introducing ConvolutionsIESTI01 TinyML Class 11 - CNN RecapIESTI01 TinyML Class 12 - Preventing OverfittingIESTI01 TinyML Class 13 - AI Lifecycle and ML WorkflowIESTI01 TinyML Class 14 - AI Lifecycle and ML WorkflowIESTI01 TinyML Class 15 - Introduction to Edge Pulse StudioIESTI01 TinyML Class 16 - Gesture Classification - Edge Pulse Studio Project IESTI01 TinyML Class 17 - Anomaly Detection with TinyML IESTI01 TinyML Class 18 - Data Engineering for TinyML IESTI01 TinyML Class 19 - TinyML Kit Overview, Installation and Test IESTI01 TinyML Class 20 - Gesture Classification & Anomaly Detection with Arduino Nano-33 BLEIESTI01 TinyML Class 21 - Keyword Spotting (KWS) IntroductionIESTI01 TinyML Class 22 - Keyword Spotting (KWS) - Edge Impulse StudioIESTI01 TinyML Classes 23 and 24 - ML for the physical worldGuest Lecturer: Daniel Situnayake, Edge Impulse
IESTI01 TinyML Class 25 - Visual Wake Words - Introduction
IESTI01 TinyML Class 26 - VWW - Demo/Lab
IESTI01 TinyML Class 27 - Parte A - Image Classification using Edge Impulse Studio
IESTI01 TinyML Class 27 - Parte B - Arduino Code Walkthrough
IESTI01 TinyML Class 28 - VWW - Demo/Lab
IESTI01 TinyML Class 28 - "Privacy in Context" by Susan Kennedy (Harvard)
IESTI01 TinyML Classes 29 and 30 - Group Presentations
» Group 1 - Seismic Detection
» Group 2 - Fire Detection
» Group 3 - Covid Detection (cough)
» Group 4 - Mask Detection
» Group 5 - Personal Trainer
TEXTOS BÁSICOS E TUTORIAIS
Papers - Artificial Intelligence Vs Machine Learning Vs Deep Learning: What exactly is the difference ?
GUIDE - Machine Learning is Fun! - Part 1 by Adam Geitgey at medium.com (05/05/2014).
The world’s easiest introduction to Machine Learning
GUIDE - Machine Learning is Fun! Part 2 by Adam Geitgey at medium.com (03/01/2016).
Using Machine Learning to generate Super Mario Maker levels
GUIA - Aprendizagem de Máquina é Divertido! Parte 3 por Adam Geitgey em medium.com (13/06/2016).
Deep Learning e Redes Neuronais Convolutivas
GUIA - Aprendizagem de Máquina é Divertido! Parte 4 por Adam Geitgey em medium.com (24/07/2016).
Reconhecimento Facial Moderno com Deep Learning
GUIDE - Machine Learning is Fun! Part 5 by Adam Geitgey at medium.com (21/08/2016).
Language Translation with Deep Learning and the Magic of Sequences
GUIDE - Machine Learning is Fun! Part 6 by Adam Geitgey at medium.com (23/12/2016).
How to do Speech Recognition with Deep Learning
GUIDE - Machine Learning is Fun! Part 7 by Adam Geitgey at medium.com (12/02/2017).
Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
Deep Convolutional Generative Adversarial Networks (DCGANs)
GUIDE - Machine Learning is Fun! Part 8 by Adam Geitgey at medium.com (16/08/2017).
How to Intentionally Trick Neural Networks
A Look into the Future of Hacking
GUIDE - How to Build a Machine Learning Model - by Chanin Nantasenamat - August 14, 2020
A Visual Guide to Learning Data Science. .
MODELAGEM - Machine Learning Models Deployment - - by Victor Roman - April 28, 2021
What is it, why does it matter, and its main challenges. .
RECONHECIMENTO FACIAL - O que é reconhecimento facial? por Kaspersky
Definição e explicação. .
TEXTOS BÁSICOS - AI & Machine Learning Insights
(Experfy at Harvard Innovation Lab).
The world’s easiest introduction to Machine Learning
Using Machine Learning to generate Super Mario Maker levels
Deep Learning e Redes Neuronais Convolutivas
Reconhecimento Facial Moderno com Deep Learning
Language Translation with Deep Learning and the Magic of Sequences
How to do Speech Recognition with Deep Learning
Abusing Generative Adversarial Networks to Make 8-bit Pixel Art
Deep Convolutional Generative Adversarial Networks (DCGANs)
How to Intentionally Trick Neural Networks
A Look into the Future of Hacking
A Visual Guide to Learning Data Science. .
What is it, why does it matter, and its main challenges. .
Definição e explicação. .
(Experfy at Harvard Innovation Lab).
PROGRAMAS DE PESQUISA, DESENVOLVIMENTO E INOVAÇÃO
P&D - PROGRAMA IA² MCTI.
Site oficial do programa IA² : www.softex.br/iamcti.
P&D - AI Next Campaign.
P&D - New DARPA program would create real-time responsive AI.
P&D - Science of Artificial Intelligence and Learning for Open-world Novelty (SAIL-ON).
ASSOCIAÇÕES, CONSÓRCIOS, PARCERIAS, ALIANÇAS E COMUNIDADES
PARCERIA - C4AI * Centro de pesquisa para inteligência artificial avançada no Brasil - Research center for advanced artificial intelligence in Brazil..
Parceria entre USP, FAPESP e IBM.
ALLIANCE - European AI Alliance.
ASSOCIAÇÃO - Japan Deep Learning Association.
COMUNIDADE - DEEP LEARNING BRASIL.
Deep Learning Brasil é uma comunidade inter-institucional que teve início na Universidade Federal de Goiás. Dentre os membros está Anderson da Silva Soares: Professor Ph.D. e pesquisador de Machine Learning da Universidade Federal de Goiás, com pesquisas em reconhecimento de padrões e meta-heurísticas e problemas de inteligência artificial.
Parceria entre USP, FAPESP e IBM.
Deep Learning Brasil é uma comunidade inter-institucional que teve início na Universidade Federal de Goiás. Dentre os membros está Anderson da Silva Soares: Professor Ph.D. e pesquisador de Machine Learning da Universidade Federal de Goiás, com pesquisas em reconhecimento de padrões e meta-heurísticas e problemas de inteligência artificial.
BLOGs
Blog - AI Google Blog .
Blog do Professor Marcelo José Rovai com projetos, práticas e tutoriais.
* - Compartilhamento de idéias e experiências no mundo da Eletrônica. Ênfase no uso de plataformas de desenvolvimento baseadas em microcontroladores, como Arduino e computadores completos do tamanho de cartões de crédito como o Raspberry-Pi.
* - Blog voltado para Eletrônica, Telecomunicações, Automação e tecnologias emergentes. Pesquisa, desenvolvimento, inovação, negócios e formação profissional. Aprendizagem, inspiração e criatividade.
HISTÓRIA DA IA ( AI HISTORY )
História - Faça uma viagem pela história da Inteligência Artificial e aprenda sobre as datas-chave que moldaram a IA. Elaborada pela AiLab.
GLOSSÁRIOS, FOLHAS DE REFERÊNCIAS E DEFINIÇÕES
Glossário - Folha de referências: glossário de IA .
Cartilha de IA de consulta rápida sobre os principais termos de inteligência artificial elaborada pelo The Enterprisers Project.
Glossário - AiLab Artificial Intelligence Glossary.
The AiLab Artificial Intelligence Glossary lists common terms used throughout the field of AI..
Cartilha de IA de consulta rápida sobre os principais termos de inteligência artificial elaborada pelo The Enterprisers Project.
The AiLab Artificial Intelligence Glossary lists common terms used throughout the field of AI..