Paul Matthews, freelance tech writer, writes about examples of machine learning across the the healthcare, entertainment, marketing and education industries. From front end development to the automotive sector, the applications of machine learning for business purposes is bigger than ever. Examples of machine learning in the real world include recommendation engines, object detection, natural language processing, and more. Let's analyse five interesting examples of how ML is being used to deliver better online experiences and advance healthcare and education. Companies like Universal, Sony and EMI have gone through a massive digitalization in the past 10 years, evolving from "selling a certain number of records" to "streaming that record for'n' number of times". Monetization via ads through plays on portals like Youtube, Spotify and Soundcloud is something sought after.

Artificial intelligence is one of the hottest topics in corporate America. So it's no surprise that companies are rushing to find the talent to support the push to adopt the advanced tech. Demand for AI specialists grew 74% in the last five years and is expected to be one of the most highly sought-after roles in 2020, according to a new study from LinkedIn. Among the necessary skills for the position are machine learning and natural language processing. But it's not just AI experts that are in high-demand.

Continuous learning and applying our knowledge can be powerful and critical success factors for achieving our professional goals. The Cognitive Class AI offers a wide variety of professional learning paths, as free of charge, to learners globally. In this article, I provide you with some prominent learning path samples with links so that you commence achieving your 2020 professional education and career development goals. I also provide you with a list of sample industry badges that you can earn by undertaking these online training courses. The badges can help you promote your knowledge, skills, experience, and expertise globally hosted in a centralised industry recognised digital program governance organisation such as Credly's Acclaim which is the world's largest network of individuals and organizations using verified achievements to unlock opportunities. You can join millions of professionals in sharing your achievements online with a simple link.

On Tuesday, RISC-V CPU fixer SiFive announced it's working with CEVA, which licenses technology for deep learning, audio, and computer vision, to simplify the creation of processors capable of handling machine learning code without demanding too much power. RISC-V is an open, royalty-free instruction set architecture, unlike Intel's x86 chip architecture which requires a license to implement recent processor designs. SiFive provides clients with access to the necessary intellectual property licenses to create custom silicon with minimal negotiation and hassle. CEVA does much the same in a more specific set of domains. SiFive and CEVA anticipate that making it easy to design low-power SoCs tuned for AI-oriented tasks will attract hardware vendors looking to sell RISC-based hardware for applications demanding on-device neural networks, like imaging, computer vision, speech recognition, and sensor data handling.

Tensor is just a multi-dimensional matrix. A tensor is usually a matrix of dimension 3 or higher. Scalar 1, a vector also known as a list or array [1,2,3], a two by two matrix [[1,2],[3,4]], tensor [ [[1,2],[3,4]], [[5,6],[7,8]] ]. A vector contains a bunch of scalars. A matrix contains a bunch of vectors. A tensor contains a bunch of matrices. You can check the data type of a variable in python using type(variable_name). In pytorch this will return the specific type of torch.tensor.

AI has the capacity to make decisions in real-time, based on pre-installed algorithms and efficient computing technologies. With an HR department encompassing the human element and AI, companies can provide an enhanced experience for their candidates and employees, writes Khalid Durrani, Digital Marketing Manager, Cubix. "Deep-learning will transform every single industry," said Andrew Ng, a Chinese-American scientist excelling in machine learning and AI. McKinsey's forecast on machine learning backs up his statement claiming that by 2030, AI will have a significant impact of $13 trillion on the global economy. HR professionals understand the importance of optimizing the combination of the human mind and machine learning for a seamless workflow and intuitive work environment.

An improved online learning algorithm for general fuzzy min-max neural network Thanh Tung Khuat Advanced Analytics Institute University of T echnology Sydney Sydney, Australia thanhtung.khuat@student.uts.edu.au Abstract --This paper proposes an improved version of the current online learning algorithm for a general fuzzy min-max neural network (GFMM) to tackle existing issues concerning expansion and contraction steps as well as the way of dealing with unseen data located on decision boundaries. These drawbacks lower its classification performance, so an improved algorithm is proposed in this study to address the above limitations. The proposed approach does not use the contraction process for overlapping hyperboxes, which is more likely to increase the error rate as shown in the literature. The empirical results indicated the improvement in the classification accuracy and stability of the proposed method compared to the original version and other fuzzy min-max classifiers. In order to reduce the sensitivity to the training samples presentation order of this new online learning algorithm, a simple ensemble method is also proposed. I NTRODUCTION Artificial neural networks (ANNs) are one of the most widely used methods for dealing with classification problems as well as real-world applications [1]. However, the main disadvantage of the original ANNs is that they do not have the capability of giving explanations of their predictive results to humans explicitly. This drawback restricts the widespread use of the ANNs for critical domains such as healthcare and criminal justice [2]. In a recent study, Rudin [2] has highlighted that there is a high demand for interpretable models to substitute black-box models in assisting decision-makers in areas with the requirement of high safety and trust.

In previous LI articles I've written, I've discussed the intersections and similarities between human learning and machine learning/artificial intelligence (ML/AI). Others have written about the similarities and differences in the learning process itself. After several requests for my insights, I've decided it's time to write about the topic everyone has been asking experts in the learning field recently, namely, how ML/AI facilitates or augments human learning. To quote Elizabeth Barrett Browning, let me count the ways. When laypeople think about ML/AI, many people describe the commonly used recommender systems popularized by Netflix, Amazon and many, many others.

You will also gain a greater understanding of the working relationship between humans and AI and what skills are predicted to be needed to work and interact with the technology-- As well as the new jobs that artificial intelligence has and will create. With this understanding, you will be able to hone your own skills to adapt your career to thrive within the future workplace. Technology now exists that can monitor natural disasters and provide warning signals earlier and more accurately to help reduce the number of casualties, or help the emergency services quickly locate victims. Technology now exists that allows people to purchase items simply by taking a picture. These are all examples of artificial intelligence.

NEW, 3.8 (5 ratings), Created by Jitesh Khurkhuriya, Jitesh's Data Science & Machine Learning Team, English [Auto-generated] Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning. In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.