While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal relationships is to use randomized controlled experiments (RCT); in many situations, however, these are impractical or sometimes unethical. Causal learning from observational data offers a promising alternative. While being relatively recent, causal learning aims to go far beyond conventional machine learning, yet several major challenges remain. Unfortunately, advances are hampered due to the lack of unified benchmark datasets, algorithms, metrics, and evaluation service interfaces for causal learning. In this paper, we introduce {\em CausalBench}, a transparent, fair, and easy-to-use evaluation platform, aiming to (a) enable the advancement of research in causal learning by facilitating scientific collaboration in novel algorithms, datasets, and metrics and (b) promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. CausalBench provides services for benchmarking data, algorithms, models, and metrics, impacting the needs of a broad of scientific and engineering disciplines.

Mathematical models and algorithms are an essential part of mathematical research data, as they are epistemically grounding numerical data. In order to represent models and algorithms as well as their relationship semantically to make this research data FAIR, two previously distinct ontologies were merged and extended, becoming a living knowledge graph. The link between the two ontologies is established by introducing computational tasks, as they occur in modeling, corresponding to algorithmic tasks. Moreover, controlled vocabularies are incorporated and a new class, distinguishing base quantities from specific use case quantities, was introduced. Also, both models and algorithms can now be enriched with metadata. Subject-specific metadata is particularly relevant here, such as the symmetry of a matrix or the linearity of a mathematical model. This is the only way to express specific workflows with concrete models and algorithms, as the feasible solution algorithm can only be determined if the mathematical properties of a model are known. We demonstrate this using two examples from different application areas of applied mathematics. In addition, we have already integrated over 250 research assets from applied mathematics into our knowledge graph.

For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This contributes to making mathematical research data FAIR by introducing semantic technology and documenting the mathematical foundations accordingly. Using the concrete example of microfracture analysis of porous media, it is shown how the knowledge of the underlying mathematical model and the corresponding numerical algorithms for its solution can be represented by the ontologies.

When it comes to understanding and applying machine learning, datasets are a key piece of the puzzle. Simply put, datasets are collections of data that can be used to train models, perform analysis, and draw conclusions. Datasets have become an invaluable tool to gain insight into various aspects of machine learning research and development. The most common type of dataset used in machine learning is a labeled dataset. Labeled datasets contain prelabeled data that has been properly formatted according to a certain set of criteria.

Simple temporal problems represent a powerful class of models capable of describing the temporal relations between events that arise in many real-world applications such as logistics, robot planning and management systems. The classic simple temporal problem permits each event to have only a single release and due date. In this paper, we focus on the case where events may have an arbitrarily large number of release and due dates. This type of problem, however, has been referred to by various names. In order to simplify and standardize nomenclatures, we introduce the name Simple Disjunctive Temporal Problem. We provide three mathematical models to describe this problem using constraint programming and linear programming. To efficiently solve simple disjunctive temporal problems, we design two new algorithms inspired by previous research, both of which exploit the problem's structure to significantly reduce their space complexity. Additionally, we implement algorithms from the literature and provide the first in-depth empirical study comparing methods to solve simple disjunctive temporal problems across a wide range of experiments. Our analysis and conclusions offer guidance for future researchers and practitioners when tackling similar temporal constraint problems in new applications. All results, source code and instances are made publicly available to further assist future research.

Trax, an end-to-end library for deep learning developed by Google. It is designed to be easy to use, with clear with good speed, with the ability to run on modern hardware such as GPUs and TPUs.The Google Brain team actively uses and maintains Trax. It is built on top of the JAX and TensorFlow numpy, which provides automatic differentiation, a set of numerical operations, and support for GPU acceleration. It includes a wide range of pre-built models and algorithms. In addition to its extensive selection of models and algorithms, it also has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. The following code creates a Transformer model for machine translation, initialises it with pre-trained weights, tokenizes an input sentence, decodes the model's output, and then detokenizes the output to get the translation.

In this blog, we have gathered the top 10 machine learning books. Learning this subject is a challenge for beginners. Take your learning experience one step ahead with these top-rated ML books on Amazon. Machine Learning: 4 Books in 1 is a complete guide for beginners to master the basics of Python programming and understand how to build artificial intelligence through data science. This book includes four books: Introduction to Machine Learning, Python Programming for Beginners, Data Science for Beginners, and Artificial Intelligence for Beginners.

Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work. Welcome to the "Python Programming: Machine Learning, Deep Learning Python" course. In this course, we will learn what is Deep Learning and how does it work.

With mission-critical operations, artificial intelligence (AI) has the potential to produce incredible benefits – not only for businesses but also for the people they serve and employ. You see it when systems detect fraudulent purchases and keep a consumer's account safe. It's in autonomous and self-driving cars, which are programmed to help keep drivers safe and avoid collisions. In each of these examples, AI is a tool to learn complex patterns – including some that are practically undetectable. The result is more impactful and, with appropriate oversight, better and fairer decision-making.

Python instructors on Udemy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels. Machine learning is constantly being applied to new industries and new problems. Whether you're a marketer, video game designer, or programmer, this course is here to help you apply machine learning to your work. Welcome to the "Python Programming: Machine Learning, Deep Learning Python" course. In this course, we will learn what is Deep Learning and how does it work.