Explainable AI (XAI) is key to establishing trust among users and fighting the black-box nature of machine learning models. In general, XAI enhances accountability and reliability in machine learning models. For a long time, tech giants like Google, IBM and others have poured resources on explainable AI to explain the decision-making process of such models. Below are the top free resources to understand Explainable AI (XAI) in detail. About: Explainable Machine Learning with LIME and H2O in R is a hands-on, guided introduction to explainable machine learning.

We start with some imports I am using PIL (pillow) in order to create the images and pascal (PascalVoc) in order to save the information as annotations. I downloaded a few images of orange trees from the web and started to sample pixels.

Needs are changing with time and so the technology. Self-driving cars, or Siri, what do these have in common? Well! they are largely the examples of machine learning being utilized as a part of this real world. Machine learning today has changed the way we look and the way we interact with the technology. Even the healthcare sector is getting transformed by the ability to record massive amounts of information about individual patients, the enormous volume of data being collected is impossible for human to analyze.

Topological Spatial Model Checking is a recent paradigm that combines Model Checking with the topological interpretation of Modal Logic. The Spatial Logic of Closure Spaces, SLCS, extends Modal Logic with reachability connectives that, in turn, can be used for expressing interesting spatial properties, such as "being near to" or "being surrounded by". SLCS constitutes the kernel of a solid logical framework for reasoning about discrete space, such as graphs and digital images, interpreted as quasi discrete closure spaces. In particular, the spatial model checker VoxLogicA, that uses an extended version of SLCS, has been used successfully in the domain of medical imaging. However, SLCS is not restricted to discrete space. Following a recently developed geometric semantics of Modal Logic, we show that it is possible to assign an interpretation to SLCS in continuous space, admitting a model checking procedure, by resorting to models based on polyhedra. In medical imaging such representations of space are increasingly relevant, due to recent developments of 3D scanning and visualisation techniques that exploit mesh processing. We demonstrate feasibility of our approach via a new tool, PolyLogicA, aimed at efficient verification of SLCS formulas on polyhedra, while inheriting some well-established optimization techniques already adopted in VoxLogicA. Finally, we cater for a geometric definition of bisimilarity, proving that it characterises logical equivalence.

Scikit-learn is one of the most popular machine leaning libraries for Python. It provides many unsupervised and supervised learning algorithms that make machine leaning simpler. We just published a scikit-learn course on the freeCodeCamp.org This course will teach you the basics of scikit-learn so you can start using it in your own machine learning projects. Vincent D. Warmerdam created this course.

Machine learning is one of the most popular buzzwords right now, and it has grown in popularity over the years. However, there is a scarcity of qualified Machine Learning professionals on the market, so now is an excellent time to begin your career in this area. This article is written to provide you with a step-by-step guide to getting started with machine learning training in Python since it is regarded as the most common programming language for machine learning. Python is a high-level object-oriented programming language that was first introduced in 1991. Python is a very readable and powerful programming language.

Scientists have developed software that can look minutes into the future. Gravity in this galaxy [even outside our solar system] behaves as predicted by Albert Einstein's general theory of relativity, confirming the theory's validity on galactic scales. FYI: Our Sun is just ONE STAR among the hundreds of billions of stars in our Milky Way Galaxy. The technology is being built into the official postal system in countries like Mongolia, Ivory Coast and Nigeria, and Mercedes, an investor, is incorporating "What3Words" navigation into its cars. FYI: Each 10-foot-square patch of Planet Earth is labeled with three words -- 57 trillion squares altogether.

Deep learning has made a remarkable impact in the field of natural image processing over the past decade. Consequently, there is a great deal of interest in replicating this success across unsolved tasks in related domains, such as medical image analysis. Core to medical image analysis is the task of semantic segmentation which enables various clinical workflows. Due to the challenges inherent in manual segmentation, many decades of research have been devoted to discovering extensible, automated, expert-level segmentation techniques. Given the groundbreaking performance demonstrated by recent neural network-based techniques, deep learning seems poised to achieve what classic methods have historically been unable. This paper will briefly overview some of the state-of-the-art (SoTA) neural network-based segmentation algorithms with a particular emphasis on the most recent architectures, comparing and contrasting the contributions and characteristics of each network topology. Using ultrasonography as a motivating example, it will also demonstrate important clinical implications of effective deep learning-based solutions, articulate challenges unique to the modality, and discuss novel approaches developed in response to those challenges, concluding with the proposal of future directions in the field. Given the generally observed ephemerality of the best deep learning approaches (i.e. the extremely quick succession of the SoTA), the main contributions of the paper are its contextualization of modern deep learning architectures with historical background and the elucidation of the current trajectory of volumetric medical image segmentation research.

This course teaches you about the most common & popular technique used in Data Science & Machine Learning: Linear Regression. You will learn the theory as well as applications of different types of linear regression models. At the end of the course, you will completely understand and know how to apply & implement in R linear models, how to run model's diagnostics, and how to know if the model is the best fit for your data, how to check the model's performance and to make predictions. Linear regression is the simplest machine learning (and thus deep learning) model you can learn, yet there is so much depth that you'll be returning to it for years to come. Learn how to test the model's fit, how to select the most suitable linear models for your data, and make predictions You'll start by absorbing the most valuable Linear Regression basics, and techniques and slowly moving to more complex assignments.

This course is a perfect fit for you. This course will take you step by step into the world of Machine Learning. Machine Learning is the study of computer algorithms that automates analytical model building. It is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine Learning is actively being used today, perhaps in many more places than one world expects.