Created by John Elder 1.5 hours on-demand video course In this course I'll teach you how to make graphical user interfaces for Python using TKinter, and how to connect those apps to the ChatGPT Artificial Intelligence API. You'll be surprised just how quickly you can create some pretty cool looking apps! You'll be able to type questions to ChatGPT straight from your app, and receive a response that is output to the screen of your app. Finally, I'll discuss how to connect to ChatGPT with an API Key, query the engine, and parse the responses in the correct way. If you've seen ChatGPT recently and want to learn how to use it programmatically, then this is the course for you!

We consider the problem of distributed pose graph optimization (PGO) that has important applications in multi-robot simultaneous localization and mapping (SLAM). We propose the majorization minimization (MM) method for distributed PGO ($\mathsf{MM-PGO}$) that applies to a broad class of robust loss kernels. The $\mathsf{MM-PGO}$ method is guaranteed to converge to first-order critical points under mild conditions. Furthermore, noting that the $\mathsf{MM-PGO}$ method is reminiscent of proximal methods, we leverage Nesterov's method and adopt adaptive restarts to accelerate convergence. The resulting accelerated MM methods for distributed PGO -- both with a master node in the network ($\mathsf{AMM-PGO}^*$) and without ($\mathsf{AMM-PGO}^{\#}$) -- have faster convergence in contrast to the $\mathsf{AMM-PGO}$ method without sacrificing theoretical guarantees. In particular, the $\mathsf{AMM-PGO}^{\#}$ method, which needs no master node and is fully decentralized, features a novel adaptive restart scheme and has a rate of convergence comparable to that of the $\mathsf{AMM-PGO}^*$ method using a master node to aggregate information from all the other nodes. The efficacy of this work is validated through extensive applications to 2D and 3D SLAM benchmark datasets and comprehensive comparisons against existing state-of-the-art methods, indicating that our MM methods converge faster and result in better solutions to distributed PGO.

Fortune ranked the University of Alabama at Birmingham's in-person master's degree in cybersecurity as the No. 1 program in the country. According to Fortune, there are nearly 770,000 cybersecurity job openings in the United States. "We are proud to be recognized for academic excellence by Fortune and named the nation's leading institution for graduate studies in cybersecurity," said UAB Provost and Senior Vice President for Academic Affairs Pam Benoit. "UAB's Department of Computer Science has created an outstanding collaborative master's degree program that prepares students to lead careers solving the world's most challenging cybersecurity problems." Fortune's first-ever ranking of in-person cybersecurity master's degree programs compared 14 programs across the United States in three components: Selectivity Score, Success Score and Demand Score.

However, what cannot go unnoticed is the fact that artificial intelligence is the key that unleashes its power. The importance of AI in almost every field has brought in a heap of opportunities for people who are looking forward to making a promising career. You need to have a fair understanding about AI to land a decent job. Taking this into account, there are countless AI courses and programs available that one can rely on. Which one to choose among the lot has always been a question. Well, we have got you covered.

Data science is a rapidly growing field with many career opportunities. Data scientists are at the forefront of solving complex problems using data-driven approaches, from predicting market trends to developing personalized recommendations. To succeed in this field, you'll need a strong foundation in mathematics, statistics, and computer science and the ability to work with large and complex datasets. The demand for skilled data scientists is high, and the earning potential is significant. According to Glassdoor, the median salary for a data scientist is over $100,000 per year. With such promising career prospects, it's no wonder that so many people are interested in pursuing data science courses. Although most data scientist jobs require you to have a bachelor's or master's degree in a related field, several jobs in the data science domain are open to those who have the right skills or experience.

At Netcentric, we come to work every day knowing we're part of the solution to the most complex challenges brands have ever faced: digital transformation. Consumer expectation of brands is increasing in a world that is more connected and fast-paced. Netcentric is a dynamic and innovative service provider with a unique culture. We empower our employees to use their creativity, looking beyond tools and technology to unlock the full potential of the Adobe Experience Cloud, so that we can deliver visionary digital marketing solutions for the world's most recognized brands. As part of the Cognizant Digital Business, we reap the benefits of combined expertise and access to multidisciplinary teams, forging ahead to become a leading customer experience player in Europe.

API development can be a time-consuming and complex task, but it doesn't have to be. With the advancements in natural language processing and machine learning, we now have access to tools like ChatGPT that can greatly simplify the process. In this blog post, we'll be taking a step-by-step approach to using ChatGPT to generate a Flask REST API. We'll cover everything from setting up…

This work presents a new multimodal system for remote attention level estimation based on multimodal face analysis. Our multimodal approach uses different parameters and signals obtained from the behavior and physiological processes that have been related to modeling cognitive load such as faces gestures (e.g., blink rate, facial actions units) and user actions (e.g., head pose, distance to the camera). The multimodal system uses the following modules based on Convolutional Neural Networks (CNNs): Eye blink detection, head pose estimation, facial landmark detection, and facial expression features. First, we individually evaluate the proposed modules in the task of estimating the student's attention level captured during online e-learning sessions. For that we trained binary classifiers (high or low attention) based on Support Vector Machines (SVM) for each module. Secondly, we find out to what extent multimodal score level fusion improves the attention level estimation. The mEBAL database is used in the experimental framework, a public multi-modal database for attention level estimation obtained in an e-learning environment that contains data from 38 users while conducting several e-learning tasks of variable difficulty (creating changes in student cognitive loads).

Quality education, one of the seventeen sustainable development goals (SDGs) identified by the United Nations General Assembly, stands to benefit enormously from the adoption of artificial intelligence (AI) driven tools and technologies. The concurrent boom of necessary infrastructure, digitized data and general social awareness has propelled massive research and development efforts in the artificial intelligence for education (AIEd) sector. In this review article, we investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We do this through the lens of a novel categorization approach. We consider the involvement of AI-driven methods in the education process in its entirety - from students admissions, course scheduling etc. in the proactive planning phase to knowledge delivery, performance assessment etc. in the reactive execution phase. We outline and analyze the major research directions under proactive and reactive engagement of AI in education using a representative group of 194 original research articles published in the past two decades i.e., 2003 - 2022. We discuss the paradigm shifts in the solution approaches proposed, i.e., in the choice of data and algorithms used over this time. We further dive into how the COVID-19 pandemic challenged and reshaped the education landscape at the fag end of this time period. Finally, we pinpoint existing limitations in adopting artificial intelligence for education and reflect on the path forward.

Learning is something that we have to do every day. As a developer, you need to learn the latest and hottest technologies because if you don't, you might not be able to succeed in this field. I am a web developer and machine learning engineer. I am working on both and trying to improve my skills with time, but sometimes you need guidance to see where you are right now and where you see yourself in the future. So for this blog, I am going to give you a perfect road map through which you can learn the basics and then start training your own different models for machine learning.