Sparse dictionary learning (SDL) has become a popular method for adaptively identifying parsimonious representations of a dataset, a fundamental problem in machine learning and signal processing. While most work on SDL assumes a training dataset of independent and identically distributed samples, a variant known as convolutional sparse dictionary learning (CSDL) relaxes this assumption, allowing more general sequential data sources, such as time series or other dependent data. Although recent work has explored the statistical properties of classical SDL, the statistical properties of CSDL remain unstudied. This paper begins to study this by identifying the minimax convergence rate of CSDL in terms of reconstruction risk, by both upper bounding the risk of an established CSDL estimator and proving a matching information-theoretic lower bound. Our results indicate that consistency in reconstruction risk is possible precisely in the `ultra-sparse' setting, in which the sparsity (i.e., the number of feature occurrences) is in $o(N)$ in terms of the length N of the training sequence. Notably, our results make very weak assumptions, allowing arbitrary dictionaries and dependent measurement noise. Finally, we verify our theoretical results with numerical experiments on synthetic data.

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true. To be successful, organizations need to become more human than ever. Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can't be duplicated by AI or machine learning.

The Python numerical computation library called NumPy provides many linear algebra functions that may be useful as a machine learning practitioner. In this tutorial, you will discover the key functions for working with vectors and matrices that you may find useful as a machine learning practitioner. This is a cheat sheet and all examples are short and assume you are familiar with the operation being performed. You may want to bookmark this page for future reference. Linear Algebra Cheat Sheet for Machine Learning Photo by Christoph Landers, some rights reserved.

About this course: What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful when thinking about building a pipeline of machine learning models. Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.

A school in China has started a new course to teach its students how to play video games as a future profession. The school hopes the £1,470-per-year'eSports and Management' course could help ambitious teenagers cash in on China's £116 million digital gaming industry. During the first year of the course, the students in their teens and early 20s spend 50 per cent of their time gaming and the rest on study'theory lessons' to ensure they would succeed in the industry. Teenagers majoring in'eSports and Management' listen to teacher Yang Xiao explain game techniques at the school The Lanxiang Technical School, situated in the city of Jinan in eastern China, launched its eSports course last September and has attracted 50 students in its inaugural year. 'At first, many parents thought it was just about playing video games,' school director Rong Lanxiang, told AFP. 'In fact, it's not the case, eSport is developing to a very high degree and it's become an economic growth driver.'

SYSTRAN and HarvardNLP are very pleased to hold the first OpenNMT Workshop in Paris on March 2nd at Station F, followed by the first ever OpenNMT Hackathon on March 3rd at Télécom ParisTech. OpenNMT is an Open Source project providing neural technologies for different tasks such as automatic machine translation, text generation and summarization. The OpenNMT project is a collection of implementations on multiple frameworks designed to be simple to use and easy to extend, while maintaining efficiency and state-of-the-art accuracy. Registration is FREE and OPEN to both the OpenNMT community as well as anyone interested in Deep Learning applications for natural language processing. During the daylong hackathon, we will provide hands-on training, but also development sessions to share good development practices and to kick off development of new features or interfaces.

Eigendecomposition can also be used to calculate the principal components of a matrix in the Principal Component Analysis method or PCA that can be used to reduce the dimensionality of data in machine learning. Eigenvectors are unit vectors, which means that their length or magnitude is equal to 1.0. They are often referred as right vectors, which simply means a column vector (as opposed to a row vector or a left vector). A right-vector is a vector as we understand them. Eigenvalues are coefficients applied to eigenvectors that give the vectors their length or magnitude. For example, a negative eigenvalue may reverse the direction of the eigenvector as part of scaling it. A matrix that has only positive eigenvalues is referred to as a positive definite matrix, whereas if the eigenvalues are all negative, it is referred to as a negative definite matrix. Decomposing a matrix in terms of its eigenvalues and its eigenvectors gives valuable insights into the properties of the matrix. Certain matrix calculations, like computing the power of the matrix, become much easier when we use the eigendecomposition of the matrix.

It's been a typical week for typical college student BINA48. On Monday, BINA attended her robot ethics class. On Tuesday, the second-semester student had an excused absence to ring the bell at the stock exchange, and soon BINA will be assistant-teaching a kindergarten class and getting a face-lift at Hanson Robotics. But that hasn't stopped the robot, which looks like the bust of a flesh-and-blood woman, from completing a Philosophy of Love course at Notre Dame de Namur University in Belmont, California. Programmed to be social, BINA48 presented her final project along with a human student, demonstrating that the robot could retain and present a philosophical perspective on love.

Are you interested in some practical natural language processing resources? There are so many NLP resources available online, especially those relying on deep learning approaches, that sifting through to find the quality can be quite a task. But what if you've completed these, have already gained a foundation in NLP and want to move to some practical resources, or simply have an interest in other approaches, which may not necessarily be dependent on neural networks? This post (hopefully) will be helpful. This is the introductory natural language processing book, at least from the dual perspectives of practicality and the Python ecosystem.

Large parts of an upcoming update to Destiny 2 have been delayed to make them better, developers Bungie have said. A range of new features were expected to arrive in the coming weeks, including changes to the multiplayer Crucible mode and new weapons. But many of them have now been delayed so that they can be as good as they can be, according to Bungie. "With today's update we've moved a few items out to later releases – this is because we are trying to ensure each feature we add hits a sufficiently high quality bar," developers said in an update. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.