It is often desirable to study functions that depend on many variables. Multivariate calculus provides us with the tools to do so by extending the concepts that we find in calculus, such as the computation of the rate of change, to multiple variables. It plays an essential role in the process of training a neural network, where the gradient is used extensively to update the model parameters. In this tutorial, you will discover a gentle introduction to multivariate calculus. A Gentle Introduction to Multivariate Calculus Photo by Luca Bravo, some rights reserved.

Forecasting in general means to display, where this exactly is to display or predict future trends using previous or historical data as inputs to obtain an efficient and effective estimation from the predictive data. Forecasting models have different methods for different situations and evaluation procedures are also conducted. Forecasting evaluation includes a procedure to be carried out in step by step that starts with testing of assumptions, testing data and methods, replicating outputs, and accessing outputs. There are three different types of forecasting which basic types of forecasting are: qualitative techniques, time series analysis and projection, and casual models. In this course you will be introduced to Linear Regression in Python, Importing Libraries, Graphical Univariate Analysis, Boxplot, Linear Regression Boxplot, Linear Regression Outliers, Bivariate Analysis, Machine Learning Base Run and Predicting Output.

The derivative defines the rate at which one variable changes with respect to another. It is an important concept that comes in extremely useful in many applications: in everyday life, the derivative can tell you at which speed you are driving, or help you predict fluctuations on the stock market; in machine learning, derivatives are important for function optimization. This tutorial will explore different applications of derivatives, starting with the more familiar ones before moving to machine learning. We will be taking a closer look at what the derivatives tell us about the different functions we are studying. In this tutorial, you will discover different applications of derivatives.

Machine learning techniques are becoming a fundamental tool for scientific and engineering progress. These techniques are applied in contexts as diverse as astronomy and spam filtering. However, correctly applying these techniques requires careful engineering. Much attention has been paid to the technical potential; relatively little attention has been paid to the software engineering process required to bring research-based machine learning techniques into practical utility. Technology companies have supported the engineering community through machine learning frameworks such as TensorFLow and PyTorch, but the details of how to engineer complex machine learning models in these frameworks have remained hidden. To promote best practices within the engineering community, academic institutions and Google have partnered to launch a Special Interest Group on Machine Learning Models (SIGMODELS) whose goal is to develop exemplary implementations of prominent machine learning models in community locations such as the TensorFlow Model Garden (TFMG). The purpose of this report is to define a process for reproducing a state-of-the-art machine learning model at a level of quality suitable for inclusion in the TFMG. We define the engineering process and elaborate on each step, from paper analysis to model release. We report on our experiences implementing the YOLO model family with a team of 26 student researchers, share the tools we developed, and describe the lessons we learned along the way.

We present a demonstration of REACT, a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators. REACT is a data-driven tool with a user-friendly graphical interface. It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning. Furthermore, it incorporates model-agnostic explanations for bringing explainability and interpretability in the process of decision making. This paper demonstrates a use case scenario of our proposed tool using a real-world dataset and presents the design of its architecture and user interface. This demonstration focuses on the agglomerative clustering of students based on their performance (i.e., incorrect responses and hints used) during an in-class activity. This formation of clusters of students with similar strengths and weaknesses may help educators to improve their course planning by identifying at-risk students, forming study groups, or encouraging tutoring between students of different strengths.

Multimodal deep learning systems which employ multiple modalities like text, image, audio, video, etc., are showing better performance in comparison with individual modalities (i.e., unimodal) systems. Multimodal machine learning involves multiple aspects: representation, translation, alignment, fusion, and co-learning. In the current state of multimodal machine learning, the assumptions are that all modalities are present, aligned, and noiseless during training and testing time. However, in real-world tasks, typically, it is observed that one or more modalities are missing, noisy, lacking annotated data, have unreliable labels, and are scarce in training or testing and or both. This challenge is addressed by a learning paradigm called multimodal co-learning. The modeling of a (resource-poor) modality is aided by exploiting knowledge from another (resource-rich) modality using transfer of knowledge between modalities, including their representations and predictive models. Co-learning being an emerging area, there are no dedicated reviews explicitly focusing on all challenges addressed by co-learning. To that end, in this work, we provide a comprehensive survey on the emerging area of multimodal co-learning that has not been explored in its entirety yet. We review implementations that overcome one or more co-learning challenges without explicitly considering them as co-learning challenges. We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations. The various techniques employed to include the latest ones are reviewed along with some of the applications and datasets. Our final goal is to discuss challenges and perspectives along with the important ideas and directions for future work that we hope to be beneficial for the entire research community focusing on this exciting domain.

There were three workshops held at AIIDE-20, held virtually October 19-23, 2020, including Experimental AI in Games, Intelligent Narrative Technologies, and Artificial Intelligence for Strategy Games. For more information the AIIDE conference, please see aiide.org. INT returned for its 12th meeting in 2020 with two excellent keynote talks and a wide variety of topics on applying AI to games and other interactive stories. The 12th workshop on Intelligent Narrative Technologies was held this year as part of the AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment. INT brings together a multidisciplinary team of researchers interested in artificial intelligence, narrative theory, game development, psychology, social justice, and many other topics. This year's workshop featured two keynotes.

Have you always wanted to learn JavaScript but you just don't know where to start? Or maybe you have started to learn Javascript, but you just don't know how to work with basic concepts like the intermediate level JavaScript programming, object-oriented programming in JavaScript, asynchronous programming in JavaScript and JSON objects. If that Sounds Like you…. Then our complete Mastering JavaScript Essentials 2021 Novice to Professional is for You! Join 800,000 Students Who Have Enrolled in our Udemy Courses! Watch the Promo Video to see how you can Get Started Today!

… 12-week-long “Online Training and Certification Course (OTCC) in Cyber Security, Artificial Intelligence and Machine Learning (AI & ML)” conducted …

Educational technology is the use of both physical hardware, software, and educational theoretic to facilitate learning and improve performance by creating, using, and managing appropriate technological processes and resources. In EdTech, the most significant uses of AI are in content recommendation, AI-powered teaching assistants such as chat-bots performing specific tasks and accessibility functions such as text to speech and voice recognition. When used effectively, these tools are empowering teachers. Continual, formative assessment data is used as input for adaptive algorithms that power an output that is a work programme. This data can also be shared with the teacher, saving them hours in manually collecting the data and giving them eyes on the strengths and weaknesses of students.