Machine reading comprehension (MRC) aims to teach machines to read and comprehend human languages, which is a long-standing goal of natural language processing (NLP). With the burst of deep neural networks and the evolution of contextualized language models (CLMs), the research of MRC has experienced two significant breakthroughs. MRC and CLM, as a phenomenon, have a great impact on the NLP community. In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches. We propose a full-view categorization and new taxonomies on these topics. The primary views we have arrived at are that 1) MRC boosts the progress from language processing to understanding; 2) the rapid improvement of MRC systems greatly benefits from the development of CLMs; 3) the theme of MRC is gradually moving from shallow text matching to cognitive reasoning.

Graphs are one of the most important data structures for representing pairwise relations between objects. Specifically, a graph embedded in a Euclidean space is essential to solving real problems, such as object detection, structural chemistry analyses, and physical simulation. A crucial requirement to applying a graph in a Euclidean space is learning the isometric transformation invariant and equivariant features. In the present paper, we propose a set of transformation invariant and equivariant models based on graph convolutional networks (GCNs), called IsoGCNs. We demonstrate that the proposed model outperforms state-of-the-art methods on tasks related with geometrical and physical data. Moreover, the proposed model can scale up to the graphs with 1M vertices and conduct an inference faster than a conventional finite element analysis. Graph-structured data embedded in a Euclidean space can be utilized in many different fields such as object detection, structural chemistry analysis, and physical simulation. Graph neural networks (GNNs) have been introduced to deal with such data. Crucial properties of a GNN include its permutation invariance and equivariance.

This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python (V2). The reason I made this course is because there is a huge gap for many students between machine learning "theory" and writing actual code. As I've always said: "If you can't implement it, then you don't understand it". Without basic knowledge of data manipulation, vectors, and matrices, students are not able to put their great ideas into working form, on a computer. This course closes that gap by teaching you all the basic operations you need for implementing machine learning and deep learning algorithms.

Artificial Neural Networks(ANN) Made Easy Artificial Neural Networks or ANN is an information processing paradigm that is inspired by the way the biological nervous system such as brain process information by Statinfer Solutions What you'll learn ANN Hyper parameters Fine-tuning and Selecting ANN models Shallow and Deep Neural Networks Building ANN Models in Python, TensorFlow and Keras Description Course Covers below topics in detail Quick recap of model building and validation Hidden Layers in ANN Back Propagation in ANN ANN model building on Python TensorFlow Introduction Building ANN models in TensorFlow ANN hyper-parameters Regularization in ANN Activation functions Learning Rate and Momentum Optimization Algorithms Basics of Deep Learning

The way Google came from nowhere with the launch of Android in 2007 to today dominating the smartphone operating system market, is what the company is doing now with AI, some market observers suggest. Google now has an 80 percent share of the worldwide smartphone OS market, and it has seeded the AI market by making its TensorFlow software library open source, putting it at the foundation of many AI applications, suggests a recent account in Analytics Insight. Some 50 Google products use TensorFlow to build deep learning applications to help differentiate companions in Photos to refinements in the core search engine. Google has become a machine learning organization. The authors state, "Google has gone through the most recent three years constructing a gigantic platform for artificial intelligence and now they're unleashing it on the world."

So you want to learn the Mathematics for Machine Learning? Well, for Machine Learning or Deep Learning and AI, a thorough mathematical understanding is not an option. I know the options out there; prerequisites and the skills you need to become successful in Machine Learning and AI. If you want to learn Machine Learning, these classes will help you to master the mathematical foundation required for writing programs and algorithms for Machine Learning, Deep Learning and AI. My goal in this piece is to help you find the resources to gain good intuition and get you the hands-on experience you need with coding neural nets, stochastic gradient descent, and principal component analysis.

The history of machine learning has largely been a story of increasing abstraction. In the dawn of ML, researchers spent considerable effort engineering features. As deep learning gained popularity, researchers then shifted towards tuning the update rules and learning rates for their optimizers. Recent research in meta-learning has climbed one level of abstraction higher: many researchers now spend their days manually constructing task distributions, from which they can automatically learn good optimizers. What might be the next rung on this ladder?

Over the past few months, I have been collecting AI cheat sheets. From time to time I share them with friends and colleagues and recently I have been getting asked a lot, so I decided to organize and share the entire collection. To make things more interesting and give context, I added descriptions and/or excerpts for each major topic. Update: We have recently redesigned these cheat sheets into a Super High Definition PDF.

The mix of data, technology, and talent has made it feasible for the present smart systems to arrive at a basic point that drives exceptional development in AI investment. Funding for AI and machine learning startups has been developing at a yearly development pace of almost 60% since 2010 and the organizations are moving past a significant stretch of exploratory AI into a period of exponential AI. As specialists state, we are entering a "Race Against the Machine," and a "Fourth Industrial Revolution." As per Crunchbase, there are 8,705 startups and organizations today depending on AI and machine learning for their essential applications, products, and services. Practically 83% of AI and machine learning startups that Crunchbase tracks, had just three or fewer funding rounds, the most well-known being seed rounds, angel rounds, and early-stage rounds.

This series explains concepts that are fundamental to deep learning and artificial neural networks for beginners. In addition to covering these concepts, we also show how to implement some of the concepts in code using Keras, a neural network API written in Python. We will learn about layers in an artificial neural network, activation functions, backpropagation, convolutional neural networks (CNNs), data augmentation, transfer learning and much more!