Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT -- a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions. We assume only to know a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb for our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT opens the opportunity to use RL to automatically explore how to interact with IoT protocols with limited information, and paving the road for interoperable systems.

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.

Welcome to the best Natural Language Processing course on the internet! This course is designed to be your complete online resource for learning how to use Natural Language Processing with the Python programming language. In the course we will cover everything you need to learn in order to become a world class practitioner of NLP with Python. We'll start off with the basics, learning how to open and work with text and PDF files with Python, as well as learning how to use regular expressions to search for custom patterns inside of text files. Afterwards we will begin with the basics of Natural Language Processing, utilizing the Natural Language Toolkit library for Python, as well as the state of the art Spacy library for ultra fast tokenization, parsing, entity recognition, and lemmatization of text.

Introduction to Vision AI Understand Vision AI concepts, technologies, use cases and solution components computer vision is one of the hottest subfields of artificial intelligence and machine learning, given its wide variety of applications and tremendous ... This course is divided into multiple sections. Section 1 provides an introduction to the course including course outline, audience, expected outcome, and authors for the course. In section 2, we will describe what is a Vision AI and how it is being used in classifying and cataloging various image and videos. We will cover key concepts behind Vision VI and its 5 complexity levels' In section 3, we will take a series of use cases in four different categories like In Section 5, we will cover first good example of Vision AI for analyzing Payment protection Plan to interpret payroll forms. US Government released more than 1 trillion dollars to support small businesses economically impacted by COVID.

You're looking for a complete Course on Deep Learning using Keras and Tensorflow that teaches you everything you need to create a Neural Network model in Python and R, right? You've found the right Neural Networks course! Identify the business problem which can be solved using Neural network Models. Have a clear understanding of Advanced Neural network concepts such as Gradient Descent, forward and Backward Propagation etc. Create Neural network models in Python and R using Keras and Tensorflow libraries and analyze their results. How this course will help you?

Description Welcome to Deep Learning and Artificial Intelligence with Tensorflow 2 and Keras API Course. This course includes how to work with tensorflow 2 and creates Deep Learning applications with tensorflow 2 and Keras. This course guide you how to work with google colab, all the hands on work done in google colab. Many Projects included in this course like MNIST Digits Classification, MNIST Fashion data classification, Cat and Dog images Classification, Facial Expression Recognition, Leaf disease recognition, Generate Images with DCGANs(Deep Convolutional Generative Adversarial Networks) with Keras, Denoising autoencoders with Keras, TensorFlow, and Deep Learning etc. For every lecture reference notes and code file is attached in this course.

On this course, you will learn how AI technology and AI processes can help businesses with both human and automated business planning and decision-making. As you learn the concepts of data sources, knowledge acquisition and types of machine learning algorithms, you will develop an understanding of the process of moving from data to knowledge. You will then explore how this process can be used to inform your professional decision-making and business planning.

Bayesian Knowledge Tracing, a model used for cognitive mastery estimation, has been a hallmark of adaptive learning research and an integral component of deployed intelligent tutoring systems (ITS). In this paper, we provide a brief history of knowledge tracing model research and introduce pyBKT, an accessible and computationally efficient library of model extensions from the literature. The library provides data generation, fitting, prediction, and cross-validation routines, as well as a simple to use data helper interface to ingest typical tutor log dataset formats. We evaluate the runtime with various dataset sizes and compare to past implementations. Additionally, we conduct sanity checks of the model using experiments with simulated data to evaluate the accuracy of its EM parameter learning and use real-world data to validate its predictions, comparing pyBKT's supported model variants with results from the papers in which they were originally introduced. The library is open source and open license for the purpose of making knowledge tracing more accessible to communities of research and practice and to facilitate progress in the field through easier replication of past approaches.

On Day 1 our Goal is to give you an overall understanding of the Bot Ecosystem, to discover the best business application that are producing an ROI, and to do deep dives in the most essential areas. We aim to answer the most pressing questions such as'what use cases have the biggest ROI', 'what is possible given the current state of AI & NLP ', 'what is the best way for Enterprises to get started' and many more. The Live Q&A is the perfect time to ask them questions and go in depth on their topic of expertise. Your bot will be able to answer 10 FAQ questions, have a great on-boarding experience and a number of fall backs. You will learn how to design and develop a bot using NLP/NLU Platforms and deploy your bot to a website, sms, messaging channel or voice.

Machine Learning and AI Foundations: Recommendations This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations. In this course, Adam Geitgey walks you through a hands-on lab building a recommendation system that is able to suggest similar products to customers based on past products they have reviewed or purchased. The system can also identify which products are similar to each other.