The paper describes tricks to scale Bayesian network structure learning to thousands of variables. This is achieved by developing new heuristics for candidate parent set identification and the subsequent order based structure optimization. In general, the paper is clearly written and easy to read. There are issues in editing and style, but the problems do not affect readability (much). The suggested heuristics feel bit ad-hoc, thus the value of the work is eventually judged by empirical evaluation.

Neural networks (NN), also called artificial neural networks (ANN) are a subset of learning algorithms within the machine learning field that are loosely based on the concept of biological neural networks. Andrey Bulezyuk, who is a German-based machine learning specialist with more than five years of experience, says that "neural networks are revolutionizing machine learning because they are capable of efficiently modeling sophisticated abstractions across an extensive range of disciplines and industries." There are several types of neural networks. In this project, we are going to create the feed-forward or perception neural networks. Training the feed-forward neurons often need back-propagation, which provides the network with corresponding set of inputs and outputs.

Here we describe well-known chaotic sequences, including new generalizations, with application to random number generation, highly non-linear auto-regressive models for times series, simulation, random permutations, and the use of big numbers (libraries available in programming languages to work with numbers with hundreds of decimals) as standard computer precision almost always produces completely erroneous results after a few iterations -- a fact rarely if ever mentioned in the scientific literature, but illustrated here, together with a solution. It is possible that all scientists who published on chaotic processes, used faulty numbers because of this issue. This article is accessible to non-experts, even though we solve a special stochastic equation for the first time, providing an unexpected exact solution, for a new chaotic process that generalizes the logistic map. We also describe a general framework for continuous random number generators, and investigate the interesting auto-correlation structure associated with some of these sequences. References are provided, as well as fast source code to process big numbers accurately, and even an elegant mathematical proof in the last section.

Here we describe well-known chaotic sequences, including new generalizations, with application to random number generation, highly non-linear auto-regressive models for times series, simulation, random permutations, and the use of big numbers (libraries available in programming languages to work with numbers with hundreds of decimals) as standard computer precision almost always produces completely erroneous results after a few iterations -- a fact rarely if ever mentioned in the scientific literature, but illustrated here, together with a solution. It is possible that all scientists who published on chaotic processes, used faulty numbers because of this issue. This article is accessible to non-experts, even though we solve a special stochastic equation for the first time, providing an unexpected exact solution, for a new chaotic process that generalizes the logistic map. We also describe a general framework for continuous random number generators, and investigate the interesting auto-correlation structure associated with some of these sequences. References are provided, as well as fast source code to process big numbers accurately, and even an elegant mathematical proof in the last section.

Here we describe well-known chaotic sequences, including new generalizations, with application to random number generation, highly non-linear auto-regressive models for times series, simulation, random permutations, and the use of big numbers (libraries available in programming languages to work with numbers with hundreds of decimals) as standard computer precision almost always produces completely erroneous results after a few iterations -- a fact rarely if ever mentioned in the scientific literature, but illustrated here, together with a solution. It is possible that all scientists who published on chaotic processes, used faulty numbers because of this issue. This article is accessible to non-experts, even though we solve a special stochastic equation for the first time, providing an unexpected exact solution, for a new chaotic process that generalizes the logistic map. We also describe a general framework for continuous random number generators, and investigate the interesting auto-correlation structure associated with some of these sequences. References are provided, as well as fast source code to process big numbers accurately, and even an elegant mathematical proof in the last section.

The chief executive of Deutsche Bank has issued a stark warning about the impact of technology, saying a "big number" of his staff will lose their jobs as robots take over. John Cryan told an audience in Frankfurt: "In our bank we have people doing work like robots. Tomorrow we will have robots behaving like people. It doesn't matter if we as a bank will participate in these changes or not, it is going to happen." He also referred to accountants inside the bank who "spend a lot of time basically being an abacus", who would also be replaced by machines.

Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a large chunk of its workforce with robots. CEO John Cryan warned today that a'big number' of people will lose their jobs at the firm as it automates to embrace its'revolutionary spirit.' The Frankfurt, Germany-based company employs 100,000 people globally, but it's unknown how many will be laid off and replaced by machines or when the overhaul will occur. Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a large chunk of its workforce with robots. A'big number' of the company's 100,000 employees will be replaced by machines Deutsche Bank - one of the world's largest financial institutions - is gearing up to replace a'big number' of its 100,000 employees with robots.

Cryan said that it was important for the bank to embrace a "revolutionary spirit" and warned that this would mean an end to an era where accountants acted like abacuses. "We have to find new ways of employing people and maybe people need to find new ways of spending their time," he said in comments reported by the Financial Times. Cryan did not elaborate on how many of the bank's 100,000 staff may lose their jobs to a robotic rival, other than to say it would be "a big number". And he hinted that those accountants acting like abacuses were most at risk. "The truthful answer is we won't need as many people…In our banks we have people behaving like robots doing mechanical things, tomorrow we're going to have robots behaving like people."