"The field of Machine Learning seeks to answer these questions: How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
– from The Discipline of Machine Learning by Tom Mitchell. CMU-ML-06-108, 2006.
Algorithms are the smart and powerful soldier of a complex machine learning model. In other words, machine learning algorithms are the core foundation when we play with data or when it's come to training the model. In this article, you and I are going on a tour called "7 major machine learning algorithms and their application " The purpose of this tour is to either brush up the mind or to gain an essential understanding of machine learning algorithm. We will find the major answer in this tour like for what purpose machine learning algorithms works, where to use them, when to use them and how to use them. Before getting deeper let's have a brief introduction. Machine learning algorithms are mainly classified into 3 broad categories i.e supervised learning, unsupervised learning, and reinforcement learning. In supervised learning machine learning algorithms, the machine is taught by example. Here the operator provides the machine learning algorithm with the dataset. This dataset includes desired inputs and outputs variables. By the use of these set of variables, we generate a function that map inputs to desired outputs.
This post was co-written with Baptiste Rocca. Yann LeCun described it as "the most interesting idea in the last 10 years in Machine Learning". Of course, such a compliment coming from such a prominent researcher in the deep learning area is always a great advertisement for the subject we are talking about! And, indeed, Generative Adversarial Networks (GANs for short) have had a huge success since they were introduced in 2014 by Ian J. Goodfellow and co-authors in the article Generative Adversarial Nets. So what are Generative Adversarial Networks?
We are announcing the release of our state-of-the-art off-policy model-free reinforcement learning algorithm, soft actor-critic (SAC). This algorithm has been developed jointly at UC Berkeley and Google Brain, and we have been using it internally for our robotics experiment. Soft actor-critic is, to our knowledge, one of the most efficient model-free algorithms available today, making it especially well-suited for real-world robotic learning. We also release our implementation of SAC, which is particularly designed for real-world robotic systems. What makes an ideal deep RL algorithm for real-world systems?
Brazil's first digital bank Original has introduced facial recognition functionality for authentication of banking transactions. With the functionality, dubbed Liveness, customers will be able to perform operations by validating their information through their mobile phone. Facial-recognition databases used by the FBI and state police hold images of 117 million US adults, according to new research. The new tool is an addition to the call-back process for transaction verification. It allows validation of customer detail changes and transactions through basic and random movements requested by the application within the banking app.
Gradient-based optimization of absolute errors is tricky, since the gradient is "never" zero. In theory, adaptive methods should be able to damp oscillations so that it converges to the minimum. However, I found none of the'standard' methods were able to do this "out of the box". Learning rate decay could alleviate the problem, but needs manual tuning which I would rather avoid. Does anyone know of a method that can do this?
In a recent post I introduced three existing approaches to explain individual predictions of any machine learning model. After the posts focused on LIME and Shapley values, now it's the turn of Explanation vectors, a method presented by David Baehrens, Timon Schroeter and Stefan Harmeling in 2010. As we have seen in the mentioned posts, explaining a decision of a black box model implies understanding what input features made the model give its prediction for the observation being explained. Intuitively, a feature has a lot of influence on the model decision if small variations in its value cause large variations of the model's output, while a feature has little influence on the prediction if big changes in that variable barely affect the model's output. Since a model is a scalar function, its gradient points in the direction of the greatest rate of increase of the model's output, so it can be used as a measure of features' influence.
QUT researchers have developed an algorithm that can predict where a tennis player will hit the next ball by analysing Australian Open data of thousands of shots by the top male tennis players. Dr Simon Denman, a Senior Research Fellow with the Speech, Audio, Image and Video Technology Laboratory, said the research into the match play of Novak Djokovic, Rafael Nadal and Roger Federer could lead to new ways for professional tennis players to predict their opponent's moves or virtual reality games offering the chance to go head-to-head with world's best players in an accurate but artificial grand slam. Dr Denman is part of a team of QUT researchers, including PhD student Tharindu Fernando, Professor Sridha Sridharan and Professor Clinton Fookes, all from the Vision and Signal Processing Discipline at QUT, who created the algorithm for predicting the next shot in tennis using Hawk-Eye data from the 2012 Australian Tennis Open, provided by Tennis Australia. The researchers narrowed their focus to study just the shot selection of Djokovic, Nadal and Federer because they had the complete data to input into the system on how the players' shot selection changed as the tournament progressed. The researchers analysed more than 3400 shots for Djokovic, nearly 3500 shots for Nadal and almost 1900 shots by Federer, adding context for each shot such as whether it was a return, a winner or an error.
When watching a film, you may be the sort of person who immerses themselves in the story and special effects with a view to a couple of hours of escapism. Or, perhaps like me, you are the type who wants to work out what is real and what is computer generated imagery (CGI) and how realistic it really is. Either way, filmmakers continue to push the boundaries to improve the quality and variety of the special effects they deliver with the purpose of enhancing the audience experience and keep us coming back to the box-office. Films are now leaving the studio and the location shoot and moving in a steady stream towards the data center. The latest wave of technology seeing adoption includes areas such as Machine Learning and Deep Learning, which are all subcategories of artificial intelligence (AI).
While machine learning is one of the biggest buzzwords in cybersecurity and the tech industry in general, the phrase itself is often overused and mis-applied, leaving many to have their own, incorrect definition of what machine learning actually is. So, how do you cut through all the noise to separate fact from fiction? And how can this tool be best applied to security operations? Click here to view original webpage at www.helpnetsecurity.com
Predictive maintenance, predictive quality and automated root cause analysis are industry 4.0 initiatives driven by the power of AI and machine learning. However, many implementations – specifically in the process manufacturing industry – fall short in delivering the promised value of industry 4.0 due to inaccurate insights and too many false positives. With process-based machine learning, the specific characteristics of the manufacturing process and its assets are taken into consideration within the algorithms. Specific sensors in machines and mechanical equipment, production recipes, production process flows, and the facility's environmental factors – all contribute to the algorithm's accuracy. In process manufacturing, data from thousands of tags (sensors) are typically captured in a data historian.