It's back-to-school season, and because of the coronavirus pandemic, many students will be hitting the books virtually this year. Consequently, Google for Education has announced a robust set of updates that will enhance Google Meet, Google Classroom and other aspects of the service. The updates were unveiled at Google's The Anywhere School event -- but if you missed the product keynote, here's what you need to know about Google's new tools to facilitate learning in 2020. Google Meet has already seen several updates in the recent months, and updates that will make the app more accessible to teachers and students are still to come. Soon, meetings will not be able to start without a teacher present.

Edge2Learn, an e-learning company specializing in the multifamily industry, today announced the introduction of artificial intelligence to its learning platform. The AI platform provides role-playing opportunities for one-on-one instruction without a need for an in-person trainer. It is one of several cutting-edge, e-learning programs Edge2Learn offers. "The AI platform is a video-based practice training tool. It provides quick, specific feedback and an avenue to tweak sales presentations until they are perfect and ultimately result in better sales performances," said Joanna Ellis, co-founder and chief executive officer of Edge2Learn. "Several property management companies are rushing to adopt this technology, especially in the wake of COVID-19.

Online Courses Udemy The Data Science Course 2020: Complete Data Science Bootcamp, Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning Created by 365 Careers, 365 Careers Team English [Auto-generated], French [Auto-generated], 6 more Students also bought Complete Python Bootcamp: Go from zero to hero in Python 3 Statistics for Data Science and Business Analysis Python for Data Science and Machine Learning Bootcamp Intro to Data Science: Your Step-by-Step Guide To Starting Data Analysis Excel for Beginners: Statistical Data Analysis Preview this course - GET COUPON CODE Description The Problem Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. However, supply has been very limited. It is difficult to acquire the skills necessary to be hired as a data scientist.

Online Courses Udemy | Neural Networks in Python from Scratch: Complete guide, Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice! Hot & New Created by Jones Granatyr, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team English [Auto] Preview this course GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

Udemy Coupon ED Artificial Neural Network for Regression Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant - Free Course. Get Course What You'll Learn How to implement an Artificial Neural Network in Python How to do Regression How to use Google Colab Description Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch? Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we're giving you that chance for FREE. In this free course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant. The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.

Like any other language or tool, Python has some best practices to follow before, during, and after the process of writing your code. These make the code readable and create a standard across the industry. Other developers working on the project should be able to read and understand your code. We have listed out a few of these for you to follow and write cleaner and more professional code. Do you follow any of these?

As part of a perennial project, our team is actively engaged in developing new synthetic assistant (SA) technologies to assist in training combat medics and medical first responders. It is critical that medical first responders are well trained to deal with emergencies more effectively. This would require real-time monitoring and feedback for each trainee. Therefore, we introduced a voice-based SA to augment the training process of medical first responders and enhance their performance in the field. The potential benefits of SAs include a reduction in training costs and enhanced monitoring mechanisms. Despite the increased usage of voice-based personal assistants (PAs) in day-to-day life, the associated effects are commonly neglected for a study of human factors. Therefore, this paper focuses on performance analysis of the developed voice-based SA in emergency care provider training for a selected emergency treatment scenario. The research discussed in this paper follows design science in developing proposed technology; at length, we discussed architecture and development and presented working results of voice-based SA. The empirical testing was conducted on two groups as user studies using statistical analysis tools, one trained with conventional methods and the other with the help of SA. The statistical results demonstrated the amplification in training efficacy and performance of medical responders powered by SA. Furthermore, the paper also discusses the accuracy and time of task execution (t) and concludes with the guidelines for resolving the identified problems.

Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to increasingly perform more complex tasks and, therefore, be able to acquire experience from different sources as quickly as possible. A plausible approach to address this issue is interactive feedback, where a trainer advises a learner on which actions should be taken from specific states to speed up the learning process. Moreover, deep reinforcement learning has been recently widely utilized in robotics to learn the environment and acquire new skills autonomously. However, an open issue when using deep reinforcement learning is the excessive time needed to learn a task from raw input images. In this work, we propose a deep reinforcement learning approach with interactive feedback to learn a domestic task in a human-robot scenario. We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL). We demonstrate that interactive approaches provide advantages for the learning process. The obtained results show that a learner agent, using either agent-IDeepRL or human-IDeepRL, completes the given task earlier and has fewer mistakes compared to the autonomous DeepRL approach.

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample complexity of most deep reinforcement learning methods is high, precluding their use in some important applications. Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. Current deep learning methods use high-capacity networks to solve high-dimensional problems. Unfortunately, high-capacity models typically require many samples, negating the potential benefit of lower sample complexity in model-based methods. A challenge for deep model-based methods is therefore to achieve high predictive power while maintaining low sample complexity. In recent years, many model-based methods have been introduced to address this challenge. In this paper, we survey the contemporary model-based landscape. First we discuss definitions and relations to other fields. We propose a taxonomy based on three approaches: using explicit planning on given transitions, using explicit planning on learned transitions, and end-to-end learning of both planning and transitions. We use these approaches to organize a comprehensive overview of important recent developments such as latent models. We describe methods and benchmarks, and we suggest directions for future work for each of the approaches. Among promising research directions are curriculum learning, uncertainty modeling, and use of latent models for transfer learning.

Deep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have predominated in scientific computing do not scale well with dimensionality. As a result, many high-dimensional sampling and approximation problems once thought intractable are being revisited through the lens of machine learning. While the promise of unparalleled accuracy may suggest a renaissance for applications that require parameterizing representations of complex systems, in many applications gathering sufficient data to develop such a representation remains a significant challenge. Here we introduce an approach that combines rare events sampling techniques with neural network optimization to optimize objective functions that are dominated by rare events. We show that importance sampling reduces the asymptotic variance of the solution to a learning problem, suggesting benefits for generalization. We study our algorithm in the context of learning dynamical transition pathways between two states of a system, a problem with applications in statistical physics and implications in machine learning theory. Our numerical experiments demonstrate that we can successfully learn even with the compounding difficulties of high-dimension and rare data.