By Vincent Granville Ph.D, published in September 2022. The book is available here. For my upcoming course based on this book, see here. This book covers the foundations of machine learning, with modern approaches to solving complex problems. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI).

The focus of this thesis is broadly on the alignment of lexicographical data, particularly dictionaries. In order to tackle some of the challenges in this field, two main tasks of word sense alignment and translation inference are addressed. The first task aims to find an optimal alignment given the sense definitions of a headword in two different monolingual dictionaries. This is a challenging task, especially due to differences in sense granularity, coverage and description in two resources. After describing the characteristics of various lexical semantic resources, we introduce a benchmark containing 17 datasets of 15 languages where monolingual word senses and definitions are manually annotated across different resources by experts. In the creation of the benchmark, lexicographers' knowledge is incorporated through the annotations where a semantic relation, namely exact, narrower, broader, related or none, is selected for each sense pair. This benchmark can be used for evaluation purposes of word-sense alignment systems. The performance of a few alignment techniques based on textual and non-textual semantic similarity detection and semantic relation induction is evaluated using the benchmark. Finally, we extend this work to translation inference where translation pairs are induced to generate bilingual lexicons in an unsupervised way using various approaches based on graph analysis. This task is of particular interest for the creation of lexicographical resources for less-resourced and under-represented languages and also, assists in increasing coverage of the existing resources. From a practical point of view, the techniques and methods that are developed in this thesis are implemented within a tool that can facilitate the alignment task.

AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines. The book is split into four main chapters -- excluding chapter 1 (introduction) and chapter 6 (conclusion): Chapter 2 introduces neural networks and covers all the basic building blocks that are used to build deep networks such as those used by AlphaZero. Contents include the perceptron, back-propagation and gradient descent, classification, regression, multilayer perceptron, vectorization techniques, convolutional networks, squeeze and excitation networks, fully connected networks, batch normalization and rectified linear units, residual layers, overfitting and underfitting. Chapter 3 introduces classical search techniques used for chess engines as well as those used by AlphaZero. Contents include minimax, alpha-beta search, and Monte Carlo tree search. Chapter 4 shows how modern chess engines are designed. Aside from the ground-breaking AlphaGo, AlphaGo Zero and AlphaZero we cover Leela Chess Zero, Fat Fritz, Fat Fritz 2 and Efficiently Updatable Neural Networks (NNUE) as well as Maia. Chapter 5 is about implementing a miniaturized AlphaZero. Hexapawn, a minimalistic version of chess, is used as an example for that. Hexapawn is solved by minimax search and training positions for supervised learning are generated. Then as a comparison, an AlphaZero-like training loop is implemented where training is done via self-play combined with reinforcement learning. Finally, AlphaZero-like training and supervised training are compared.

The State of AI and Machine Learning Report is an annual exploration of the strategies implemented by companies large and small, across industries and continents as they advance in their AI maturity. The 8th edition of this report highlights the prevailing approaches to data management and security, responsible AI, and the significant role played by external data providers in advancing progress. As companies are advancing in AI maturity, we see an even bigger focus on ethics and data diversity.

The problem of minimizing a polynomial over a set of polynomial inequalities is an NP-hard non-convex problem. Thanks to powerful results from real algebraic geometry, one can convert this problem into a nested sequence of finite-dimensional convex problems. At each step of the associated hierarchy, one needs to solve a fixed size semidefinite program, which can be in turn solved with efficient numerical tools. On the practical side however, there is no-free lunch and such optimization methods usually encompass severe scalability issues. Fortunately, for many applications, we can look at the problem in the eyes and exploit the inherent data structure arising from the cost and constraints describing the problem, for instance sparsity or symmetries. This book presents several research efforts to tackle this scientific challenge with important computational implications, and provides the development of alternative optimization schemes that scale well in terms of computational complexity, at least in some identified class of problems. The presented algorithmic framework in this book mainly exploits the sparsity structure of the input data to solve large-scale polynomial optimization problems. We present sparsity-exploiting hierarchies of relaxations, for either unconstrained or constrained problems. By contrast with the dense hierarchies, they provide faster approximation of the solution in practice but also come with the same theoretical convergence guarantees. Our framework is not restricted to static polynomial optimization, and we expose hierarchies of approximations for values of interest arising from the analysis of dynamical systems. We also present various extensions to problems involving noncommuting variables, e.g., matrices of arbitrary size or quantum physic operators.

The eighth edition of the SIDO Lyon will open its doors on September 14 and 15, 2022 at the Cité Internationale de Lyon: 48h top-chrono to decipher the innovation and to concretize its digitalization projects, whatever the level of maturity of its project! SIDO Lyon, the leading trade show in France for IoT, AI, XR and Robotics solutions for the 4.0 transformation of companies, will take place in a few weeks. To make IoT, AI, XR and robotics technologies accessible, to promote human-machine collaboration and thus better use of these technologies by industrial and service companies, to reinvent business processes with new roles, new ways of collaborating and to drive new business models. The market leaders will be in Lyon: Microsoft, Orange, STMicroelectronics, Avnet Silica, Aquitaine Robotics, the Auvergne Rhône-Alpes Region… but also and for the first time, the Pays de la Loire Region, sponsor of the event, which will present through Proxinnov, more than 80m2 dedicated to IoT, AI and Robotics solutions in the region, including a conveyor around which several collaborative robots (or cobots) will simulate a live production line. In total, there will be more than 40% of new exhibitors to discover.

Advertiser disclosure: We may be compensated by vendors who appear on this page through methods such as affiliate links or sponsored partnerships. This may influence how and where their products appear on our site, but vendors cannot pay to influence the content of our reviews. Machine learning (ML) books are a valuable resource for IT professionals looking to expand their ML skills or pursue a career in machine learning. In turn, this expertise helps organizations automate and optimize their processes and make data-driven decisions. Machine learning books can help ML engineers learn a new skill or brush up on old ones.

Linear Algebra and Its Applications, 4th Edition Linear algebra is relatively easy for students during the early stages of the course, when the material is presented in a familiar, concrete setting. But when abstract concepts are introduced, students often hit a brick wall. Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations), are not easily understood, and require time to assimilate. Since they are fundamental to the study of linear algebra, students' understanding of these concepts is vital to their mastery of the subject. David Lay introduces these concepts early in a familiar, concrete Rn setting, develops them gradually, and returns to them again and again throughout the text so that when discussed in the abstract, these concepts are more accessible.

The Fourth Industrial Revolution, also known as Industry 4.0, represents the technological evolution from traditional manufacturing systems to cyber-physical systems, which leads to improvement of overall productivity and reductions of environmental impact, thus promoting sustainable economic development. Industry 4.0 has been driven by emerging technology developments in the field of digital twin, artificial intelligence, robotic and automation, Internet of Things (IoT), cloud computing, and edge/fog computing, and has been a hot topic in both academia and industry. The resulting big data are fed to AI-based mission-critical systems to perform effectively production monitoring, quality inspection, root cause analysis, quality prediction, and process control. The proper adoption of relevant industry 4.0 technologies should lead to significant efficiency improvement and cost reduction in various industrial sectors. The goal of this special issue is to bring together researchers and practitioners from academia and industry to provide a forum for discussing industrial automation research on smart manufacturing and machine learning.

Abstract knowledge is deeply grounded in many computer-based applications. An important research area of Artificial Intelligence (AI) deals with the automatic derivation of knowledge from data. Machine learning offers the according algorithms. One area of research focuses on the development of biologically inspired learning algorithms. The respective machine learning methods are based on neurological concepts so that they can systematically derive knowledge from data and store it. One type of machine learning algorithms that can be categorized as "deep learning" model is referred to as Deep Neural Networks (DNNs). DNNs consist of multiple artificial neurons arranged in layers that are trained by using the backpropagation algorithm. These deep learning methods exhibit amazing capabilities for inferring and storing complex knowledge from high-dimensional data. However, DNNs are affected by a problem that prevents new knowledge from being added to an existing base. The ability to continuously accumulate knowledge is an important factor that contributed to evolution and is therefore a prerequisite for the development of strong AIs. The so-called "catastrophic forgetting" (CF) effect causes DNNs to immediately loose already derived knowledge after a few training iterations on a new data distribution. Only an energetically expensive retraining with the joint data distribution of past and new data enables the abstraction of the entire new set of knowledge. In order to counteract the effect, various techniques have been and are still being developed with the goal to mitigate or even solve the CF problem. These published CF avoidance studies usually imply the effectiveness of their approaches for various continual learning tasks. This dissertation is set in the context of continual machine learning with deep learning methods. The first part deals with the development of an ...