Recent powerful pre-trained language models have achieved remarkable performance on most of the popular datasets for reading comprehension. It is time to introduce more challenging datasets to push the development of this field towards more comprehensive reasoning of text. In this paper, we introduce a new Reading Comprehension dataset requiring logical reasoning (ReClor) extracted from standardized graduate admission examinations. As earlier studies suggest, human-annotated datasets usually contain biases, which are often exploited by models to achieve high accuracy without truly understanding the text. In order to comprehensively evaluate the logical reasoning ability of models on ReClor, we propose to identify biased data points and separate them into EASY set while the rest as HARD set. Empirical results show that state-of-the-art models have an outstanding ability to capture biases contained in the dataset with high accuracy on EASY set. However, they struggle on HARD set with poor performance near that of random guess, indicating more research is needed to essentially enhance the logical reasoning ability of current models. 1

"Subjecting 5-year-olds to this technology will not make anyone safer, and we can't allow invasive surveillance to become the norm in our public spaces," said Stefanie Coyle, deputy director of the Education Policy Center for the New York Civil Liberties Union. "Reminding people of their greatest fears is a disappointing tactic, meant to distract from the fact that this product is discriminatory, unethical and not secure." The debate in Lockport has unfolded over nearly two years. The school district initially announced its plans to install a facial recognition security system, called Aegis, in March 2018. The district spent $1.4 million, with money it had been awarded by the state, to install the technology across 300 cameras.

Did you ever wonder how machines "learn" - in this course you will find out. We will cover all fields of Machine Learning: Regression and Classification techniques, Clustering, Association Rules, Reinforcement Learning, and, possibly most importantly, Deep Learning for Regression, Classification, Convolutional Neural Networks, Autoencoders, Recurrent Neural Networks, ... For each field, different algorithms are shown in detail: their core concepts are presented in 101 sessions. Here, you will understand how the algorithm works. Then we implement it together in lab sessions. We develop code, before I encourage you to work on exercise on your own, before you watch my solution examples.

Udemy Coupon - Data Science and Machine Learning Bootcamp with R, Learn how to use the R programming language for data science and machine learning and data visualization! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost!

Multi-task learning is an approach in which one model is asked to perform multiple tasks. "In our case, we wanted the model to be able to predict whether a student would answer each question on a test correctly, based on the student's behavior while playing an educational game called Crystal Island," says Jonathan Rowe, co-author of a paper on the work and a research scientist in North Carolina State University's Center for Educational Informatics (CEI). "The standard approach for solving this problem looks only at overall test score, viewing the test as one task," Rowe says. "In the context of our multi-task learning framework, the model has 17 tasks -- because the test has 17 questions." The researchers had gameplay and testing data from 181 students.

I am avoiding repeating same models with Python but included linear regression & logistic regression for continuation purpose. Going forward, I will cover other techniques with Python like image recognition, sentiment analysis etc. Image recognition is in progress & course will be updated soon with it. Unlike most machine learning courses out there, the Complete Machine Learning & Data Science with R-2019 is comprehensive. We are not only covering popular machine learning techniques but also additional techniques like ANOVA & CART techniques. Course is structured into various parts like R programming, data selection & manipulation, applied statistics & data visualization. This will help you with the structure of data science and machine learning.

Recently, I've curated a list of 50 Finnish startups in the field of AI & Machine Learning for those who are looking for business partners or companies to invest in. If you are an international investor who wants to connect with one of the startups, feel free to drop me a message. I can make the intro and provide the companies' investor pitch deck to you if it is available. You can also use Finder.fi to check the company's revenue development. If you are an ambitious entrepreneur (based in Finland) who is working on the next world-changing idea and is looking for funding, let's meet! I'll be happy to discuss how we can help you with the fundraising process (for free). Most of the following information is from the company website. But if you've spotted an error, please let me know and I will revise accordingly. "AISpotter has developed a time-saving, fast service for coaches all around the world. Our goal is to combine high-end technology and sports of any kind. With real-time analysis, coaches and teams are given the power to be one step ahead in team development." "We've taken over 30 years of recognized University of Oulu Machine Vision Group technology and adapted it to improve your sports game. By combining state-of-the-art machine learning and computer vision in our unique way, we provide automatic and fast analysis service for your game."

The education sector is constantly changing after the involvement of advanced technologies Artificial intelligence and machine learning together. A report informs that by 2021, the application of AI in education and learning will increase by 47.5%. Artificial Intelligence in education is helping in processing the tedious challenges. As these days content is are becoming more accessible through smart devices, AI is a perfect way to change the way people learn educational materials. AI is changing the scenario of the education sector while evaluating the data-driven approaches.

Model-free reinforcement learning algorithms combined with value function approximation have recently achieved impressive performance in a variety of application domains, including games and robotics. However, the theoretical understanding of such algorithms is limited, and existing results are largely focused on episodic or discounted Markov decision processes (MDPs). In this work, we present adaptive approximate policy iteration (AAPI), a learning scheme which enjoys a O(T^{2/3}) regret bound for undiscounted, continuing learning in uniformly ergodic MDPs. This is an improvement over the best existing bound of O(T^{3/4}) for the average-reward case with function approximation. Our algorithm and analysis rely on adversarial online learning techniques, where value functions are treated as losses. The main technical novelty is the use of a data-dependent adaptive learning rate coupled with a so-called optimistic prediction of upcoming losses. In addition to theoretical guarantees, we demonstrate the advantages of our approach empirically on several environments.