The U.S. Department of Education's Institute of Education Sciences has awarded the National Center for Research on Education Access and Choice (REACH) at Tulane University a $100,000 contract to collect data from approximately 150,000 school websites across the country to see how the nation's education system is responding to the coronavirus pandemic. The project, which will track traditional public schools, charter schools and private schools, aims to quickly answer questions that are critical for understanding how students are learning when school buildings are closed. Key questions include: how many schools are providing any kind of instructional support; which are delivering online instruction; what resources are they offering to students and how do students stay in contact with teachers? "This data will also help answer important questions about equity in the school system, showing how responses differ according to characteristics like spending levels, student demographics, internet access, and if there are differences based on whether it is a private, charter or traditional public school," said REACH National Director Douglas N. Harris, Schlieder Foundation Chair in Public Education and chair of economics at Tulane University School of Liberal Arts. REACH will work in cooperation with Nicholas Mattei, assistant professor of computer science at Tulane University School of Science and Engineering, to create a computer program that will collect data from every school and district website in the country.

Posted by Josh Gordon, Developer Advocate A new HarvardX TinyML course on edX.orgProf. Vijay Janapa Reddi of Harvard, the TensorFlow Lite Micro team, and the edX online learning platform are sharing a series of short TinyML courses this fall that you can observe for free, or sign up to take and receive a certificate. In this article, I'll share a bit about TinyML, what you can do with it, and the …

What is this course all about? This course is all about credit scoring / logistic regression model building using SAS. There course promises to explain concepts in a crystal clear manner. It goes through the practical issue faced by analyst. How to clarify objective and ensure data sufficiency?

Before we think about it, the question that comes to our mind is, What is programming and what should I do after learning programming? So, let's break it down into different parts. Programming is the process of creating instructions for computers to perform a task. Programming is the art of thinking clearly. There are many programming languages available like Javascript, PHP, Python, etc. Programming is used for many purposes, such as mobile apps, software, websites, games, and numerical calculations for research.

Free Coupon Discount - Python for Data Science and Machine Learning beginners, produced by -- When maths meet coding Created by Jay Shankar Bhatt Preview this Udemy Course GET COUPON CODE 100% Off Udemy Coupon .

Online Courses Udemy - Deep Reinforcement Learning 2.0, The smartest combination of Deep Q-Learning, Policy Gradient, Actor Critic, and DDPG Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto] Students also bought Unsupervised Deep Learning in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Cutting-Edge AI: Deep Reinforcement Learning in Python Ensemble Machine Learning in Python: Random Forest, AdaBoost Preview this course GET COUPON CODE Description Welcome to Deep Reinforcement Learning 2.0! In this course, we will learn and implement a new incredibly smart AI model, called the Twin-Delayed DDPG, which combines state of the art techniques in Artificial Intelligence including continuous Double Deep Q-Learning, Policy Gradient, and Actor Critic. The model is so strong that for the first time in our courses, we are able to solve the most challenging virtual AI applications (training an ant/spider and a half humanoid to walk and run across a field). To approach this model the right way, we structured the course in three parts: Part 1: Fundamentals In this part we will study all the fundamentals of Artificial Intelligence which will allow you to understand and master the AI of this course. These include Q-Learning, Deep Q-Learning, Policy Gradient, Actor-Critic and more.

With the emergence of conversational artificial intelligence (AI) agents, it is important to understand the mechanisms that influence users' experiences of these agents. We study a common tool in the designer's toolkit: conceptual metaphors. Metaphors can present an agent as akin to a wry teenager, a toddler, or an experienced butler. How might a choice of metaphor influence our experience of the AI agent? Sampling metaphors along the dimensions of warmth and competence---defined by psychological theories as the primary axes of variation for human social perception---we perform a study (N=260) where we manipulate the metaphor, but not the behavior, of a Wizard-of-Oz conversational agent. Following the experience, participants are surveyed about their intention to use the agent, their desire to cooperate with the agent, and the agent's usability. Contrary to the current tendency of designers to use high competence metaphors to describe AI products, we find that metaphors that signal low competence lead to better evaluations of the agent than metaphors that signal high competence. This effect persists despite both high and low competence agents featuring human-level performance and the wizards being blind to condition. A second study confirms that intention to adopt decreases rapidly as competence projected by the metaphor increases. In a third study, we assess effects of metaphor choices on potential users' desire to try out the system and find that users are drawn to systems that project higher competence and warmth. These results suggest that projecting competence may help attract new users, but those users may discard the agent unless it can quickly correct with a lower competence metaphor. We close with a retrospective analysis that finds similar patterns between metaphors and user attitudes towards past conversational agents such as Xiaoice, Replika, Woebot, Mitsuku, and Tay.

The Managed Care system within Medicaid (US Healthcare) uses Request For Proposals (RFP) to award contracts for various healthcare and related services. RFP responses are very detailed documents (hundreds of pages) submitted by competing organisations to win contracts. Subject matter expertise and domain knowledge play an important role in preparing RFP responses along with analysis of historical submissions. Automated analysis of these responses through Natural Language Processing (NLP) systems can reduce time and effort needed to explore historical responses, and assisting in writing better responses. Our work draws parallels between scoring RFPs and essay scoring models, while highlighting new challenges and the need for interpretability. Typical scoring models focus on word level impacts to grade essays and other short write-ups. We propose a novel Bi-LSTM based regression model, and provide deeper insight into phrases which latently impact scoring of responses. We contend the merits of our proposed methodology using extensive quantitative experiments. We also qualitatively asses the impact of important phrases using human evaluators. Finally, we introduce a novel problem statement that can be used to further improve the state of the art in NLP based automatic scoring systems.

If you're a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It's pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras. This book was a real team effort by a group of consummate professionals: Antonio Gulli (Engineering Director for the Office of the CTO at Google Cloud), Amita Kapoor (Associate Professor in the Department of Electronics at the University of Delhi), and Sujit Pal (Technology Research Director at Elsevier Labs). The resulting text, Deep Learning with TensorFlow 2 and Keras, Second Edition, is an obvious example of what happens when you enlist talented people to write a quality learning resource.

If you want to learn more about exploratory analysis using Pandas, check out Simplilearn's Data Science with Python video, which can help. We can see that columns like LoanAmount and ApplicantIncome contain some extreme values. We need to process this data using data wrangling techniques to normalize and standardize the data. We will now take a look at data wrangling using Pandas as a part of our learning of Data Science with Python. Data wrangling refers to the process of cleaning and unifying messy and complicated data sets.