Estimating value functions is a core component of reinforcement learning algorithms. Temporal difference (TD) learning algorithms use bootstrapping, i.e. they update the value function toward a learning target using value estimates at subsequent time-steps. Alternatively, the value function can be updated toward a learning target constructed by separately predicting successor features (SF)--a policy-dependent model--and linearly combining them with instantaneous rewards. We focus on bootstrapping targets used when estimating value functions, and propose a new backup target, the $\eta$-return mixture, which implicitly combines value-predictive knowledge (used by TD methods) with (successor) feature-predictive knowledge--with a parameter $\eta$ capturing how much to rely on each. We illustrate that incorporating predictive knowledge through an $\eta\gamma$-discounted SF model makes more efficient use of sampled experience, compared to either extreme, i.e. bootstrapping entirely on the value function estimate, or bootstrapping on the product of separately estimated successor features and instantaneous reward models. We empirically show this approach leads to faster policy evaluation and better control performance, for tabular and nonlinear function approximations, indicating scalability and generality.

Automated scoring of free drawings or images as responses has yet to be utilized in large-scale assessments of student achievement. In this study, we propose artificial neural networks to classify these types of graphical responses from a computer based international mathematics and science assessment. We are comparing classification accuracy of convolutional and feedforward approaches. Our results show that convolutional neural networks (CNNs) outperform feedforward neural networks in both loss and accuracy. The CNN models classified up to 97.71% of the image responses into the appropriate scoring category, which is comparable to, if not more accurate, than typical human raters. These findings were further strengthened by the observation that the most accurate CNN models correctly classified some image responses that had been incorrectly scored by the human raters. As an additional innovation, we outline a method to select human rated responses for the training sample based on an application of the expected response function derived from item response theory. This paper argues that CNN-based automated scoring of image responses is a highly accurate procedure that could potentially replace the workload and cost of second human raters for large scale assessments, while improving the validity and comparability of scoring complex constructed-response items.

Reading Comprehension (RC) is a task of answering a question from a given passage or a set of passages. In the case of multiple passages, the task is to find the best possible answer to the question. Recent trials and experiments in the field of Natural Language Processing (NLP) have proved that machines can be provided with the ability to not only process the text in the passage and understand its meaning to answer the question from the passage, but also can surpass the Human Performance on many datasets such as Standford's Question Answering Dataset (SQuAD). This paper presents a study on Reading Comprehension and its evolution in Natural Language Processing over the past few decades. We shall also study how the task of Single Document Reading Comprehension acts as a building block for our Multi-Document Reading Comprehension System. In the latter half of the paper, we'll be studying about a recently proposed model for Multi-Document Reading Comprehension - RE3QA that is comprised of a Reader, Retriever, and a Re-ranker based network to fetch the best possible answer from a given set of passages.

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Artificial intelligence (AI) is relatively new degree subject. With the rise of artificial intelligence, and the prominence of new technology, education is becoming increasingly important. A degree in artificial intelligence would allow you to be prepared to work in an area of computer science that is only going to become more utilised and improved. It is common for artificial intelligence to offered as a joint honours degree alongside a more generic computer science degree. This format ensures that you have a foundation of computer science knowledge, as well as having specialist knowledge in artificial intelligence. You will study modules in computer programming, software engineering, artificial intelligence and machine learning.

As higher education costs continue to rise, students bear the ultimate burden of choosing the right school, major, and delivery format to maximize post-graduation success. Unlike previous generations, millennials and adult learners are searching for alternatives to full-time, on-campus programs, and universities are eager to offer non-traditional routes to a degree. Distance learning programs have existed since the 1980s, but technological innovation, content scalability, and widespread mobile adoption have enabled the online degree program to be a competitive option for aspiring students. Long gone are the days of aggressive marketing tactics and empty promises made by degree mills and unaccredited for-profit universities. Today, a learner can enroll in competitive bachelor's and master's programs at U Penn, Columbia, Johns Hopkins, NYU, and more.

Infocus International Group, a global business intelligence provider of strategic information and professional services, has launched a brand-new online training – FinTech & Artificial Intelligence will be commencing live on 11 May 2022. Banking is undergoing a transformation from being based in physical branches to using information technology (IT) and big data, together with highly specialized human capital. The value proposition of Fintech is to make complex processes easy, provide guidance and automation to fulfill heavy compliance burdens, and benefit from a great richness in data. Your organization has any means to commercialize the rewards of FinTech and artificial decision-making. Participants will learn how FinTech and AI can help to work more effectively and have a greater impact on business.

"This is an ideal introduction for people who have limited time but still want to go beyond trivial, hand-waving explanations about the core concepts in deep learning. The book's focus is illustrations with a minimal amount of text. The illustrations are clear, crisp, and accurate. Moreover, they perfectly balance the text. Many books are too verbose. Some are too terse. Here, Meor strikes the perfect balance -- enough text to explain the little the illustrations don't. The book is like a CEO summary of deep learning and serves as a good starting point for people who want an overview before diving in or who simply want an overview to see what the fuss is all about."— Ronald T. Kneusel, Ph.D. (author of Practical Deep Learning: A Python-Based Introduction and Math for Deep Learning)"I am always on the lookout for effective ways to summarize concepts visually. This book takes an impressive no frills approach for people who want to learn about the underpinnings of neural networks in the most time-effective way possible."— Sebastian Raschka, Ph.D. (author of Python Machine Learning)Deep learning is the algorithm powering the current renaissance of Artificial Intelligence (AI). And the progress is not showing signs of slowing down. A McKinsey report estimates that by 2030, AI will potentially deliver $13 trillion to the global economy, or 16% of the world's current GDP. This opens up exciting career opportunities in the coming decade.But deep learning can be quite daunting to learn. With the abundance of learning resources in recent years has emerged another problem—information overload.This book aims to compress this knowledge and make the subject approachable.By the end of this book, you will be able to build a visual intuition about deep learning and neural networks. Who is this for:If you are just beginning your journey in deep learning, or machine learning in general.If you have already got started with deep learning but want to gain further intuition.If you are a leader looking to understand deep learning and AI from first principles. The book's contents are designed to help you navigate the various concepts with as little friction as possible:Each of the 235 pages is visual-led and supported by concise text.The math is kept to a minimum.The same dataset is used in all chapters so you have the same, consistent reference.The dataset is small and simple so you can 'touch and feel' it and grasp the dynamics more easily. What the Book CoversThe motivation behind deep learning and machine learning in general.Deep dive into a feedforward neural network via four tasks - linear regression, nonlinear regression, binary classification, multiclass classification. These will be demonstrated using tabular data.A quick tour of the different variants of a neural network - convolutional, recurrent and generative - and the different types of data - images, text, etc.Content Overview Table of Contents What the Book Doesn't CoverMathematical derivationsCode examplesFurther topics such as optimizers, regularization, embeddings, etc. DetailsLength: 235 pagesAuthor: Meor Amer About the AuthorMy journey into AI began in 2010 after my son was born with a limb difference. I became interested in machine learning in prosthetics and did an MSc at Imperial College London majoring in neurotechnology.I have also worked in the telecoms data analytics space, where I did solution engineering for clients in over 15 countries.Above all, I am passionate about education and how we learn. I am currently working on projects that explore ways to create alternative learning experiences using visuals, storytelling, and games.Connect with me on LinkedIn Refund PolicyThere is a 30-day refund policy. And to compensate for your time, you get to keep the book even after the refund. For any queries, send your email to contact@kdimensions.com.Reader ReviewsOne of our most advanced senses is sight. Our eyes alert us to danger, lead us to sustenance, and allow us to enjoy stories. Meor Amer is a master storyteller. In A Visual Introduction to Deep Learning, Meor is our tour guide for a journey of discovery in this amazing field of Artificial Intelligence. His hand-crafted minimalist graphics are accompanied by succinct descriptions where he illuminates the subtle hints in each picture. I enthusiastically recommend this learning resource for AI enthusiasts. — Jack CrawfordThis is an amazing visual illustration book on deep learning. It bridges the gap between textual reading and contextual thinking. You can see what you learn. It's like "things coming to life!".— Raj ArunYou have made it really simple.— Sanjay MahanaYou really did a great job in explaining the concepts and reflecting them visually.— Alia HamwiVery clear non-technical explanations of deep learning. As AI becomes more prevalent in many businesses, it’s important that leaders understand the first principles.— Emily Ryder MartinsYou can’t miss anymore the basics of this. Love this book. The visuals help a lot. Meor Amer has produced, for me, the unique foundation overview. — Francisco TosteAre you a visual learner and want to build an intuition about deep learning? Here is a good, very easy-to-read book.— Andrew YaroshevskyI have been looking for this type of formatted approach. A no-risk investment with huge rewards!— Louis Girardin

In recent years, voice-based virtual assistants such as Google Assistant and Amazon Alexa have grown popular. This has presented both potential and challenges for natural language understanding (NLU) systems. These devices' production systems are often trained by supervised learning and rely significantly on annotated data. But, data annotation is costly and time-consuming. Furthermore, model updates using offline supervised learning can take long and miss trending requests.

The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning MSc / PhD. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they're framed within thought-provoking questions and engaging stories. That is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.