Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data. One of the most popular approaches to this goal is machine reading comprehension(MRC). In recent years, many novel datasets and evaluation metrics based on classical MRC tasks have been proposed for broader textual QA tasks. In this paper, we survey 47 recent textual QA benchmark datasets and propose a new taxonomy from an application point of view. In addition, We summarize 8 evaluation metrics of textual QA tasks. Finally, we discuss current trends in constructing textual QA benchmarks and suggest directions for future work.

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.

To build increasingly general-purpose artificial intelligence systems that can deal with unknown variables across unknown domains, we need benchmarks that measure precisely how well these systems perform on tasks they have never seen before. A prerequisite for this is a measure of a task's generalization difficulty, or how dissimilar it is from the system's prior knowledge and experience. If the skill of an intelligence system in a particular domain is defined as it's ability to consistently generate a set of instructions (or programs) to solve tasks in that domain, current benchmarks do not quantitatively measure the efficiency of acquiring new skills, making it possible to brute-force skill acquisition by training with unlimited amounts of data and compute power. With this in mind, we first propose a common language of instruction, i.e. a programming language that allows the expression of programs in the form of directed acyclic graphs across a wide variety of real-world domains and computing platforms. Using programs generated in this language, we demonstrate a match-based method to both score performance and calculate the generalization difficulty of any given set of tasks. We use these to define a numeric benchmark called the g-index to measure and compare the skill-acquisition efficiency of any intelligence system on a set of real-world tasks. Finally, we evaluate the suitability of some well-known models as general intelligence systems by calculating their g-index scores.

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Wondering what are the best resources to start your Deep learning journey? Here is a curated list of collections that will save you a lot of time. Developed and taught by DeepLearning.ai in cooperation with Andrew Ng, this course will teach you all the fundamentals principles behind Deep Learning, how to develop and train models with Python and Tensorflow as well as real-world case studies. A set of high-level lectures by MIT, which provides a great overview of the field of Deep Learning. Open source code is also offered alongside video lectures, making it ideal for beginners.

Machine learning, artificial intelligence, and big data are the buzzwords of the digital world. For a very long time, we have been using machine learning technology without actually realizing it. Every online or app recommendation we get while searching on the internet or smartphone is backed by machine learning. Data science on the other hand covers a wider spectrum of domains and one of them is machine learning. After realizing the potential of big data, data science merged as a field that utilizes algorithms and mathematical calculations to reap business insights.

Coventry University is already reaching out to students who appear to be less engaged in their courses - utilising students' use of campus facilities and their digital footprint as a measure of engagement. The groundbreaking AI software, which Coventry University has co-developed from the ground up with AI firm Symanto, is designed to interpret student behaviour more accurately, enabling the university's Student Engagement Centre to step in and offer support more effectively and earlier than ever before. The technology analyses metrics such as attendance, library usage, grades and online learning activity, painting a picture of how well-engaged students are with their courses, studies and the university as a whole. It can also predict trajectories of student engagement and has the potential to continue evolving. Should students' habits or behaviours show worrying trends or sudden, dramatic changes, the university will then be alerted, before offering timely support to those who appear to need it.

My name is Samuel Brown, this is my documented progress on machine learning, deep learning, etc. I am a 5th year Electronic and Electrical Engineering student at the university of Strathclyde and although I consider myself pretty multi-disciplinary I would like my future work to include some form of machine learning or deep learning. In order for future employers to review my progress and see what I've actually done I think its important I document my experiences within the next year. As I said I'm currently in my final year (masters year) of university, so this page probably wont just be machine learning related as I want to document things I learn during this academic year too. Expect me to write about any of these subjects as well as Machine Learning/Deep Learning which I will learn in my spare time.

This "Data Structures and Algorithms In C " course is thoroughly detailed and uses lots of animations to help you visualize the concepts. This "Data Structures and Algorithms in C " tutorial will help you develop a strong background in Data Structures and Algorithms. The course is broken down into easy to assimilate short lectures, and after each topic there is a quiz that can help you to test your newly acquired knowledge. The examples are explained with animations to simplify the learning of this complex topic. Complete working programs are shown for each concept that is explained. This course provides a comprehensive explanation of data structures like linked lists, stacks and queues, binary search trees, heap, searching, hashing.

Before presenting employer certification-demand findings, it is necessary to describe the methodology in order to assist in interpreting the results. The certification-demand analysis was performed using the Economic Modeling Specialists International (EMSI) dataset. To populate the dataset, EMSI combs through 100,000 websites, effectively capturing job listings for more than 1.5 million companies. The same job listings regularly appear on multiple websites. To reduce duplicates, EMSI uses a machine learning-based duplicate-detection process.