This article comes from Algobeans Layman tutorials in analytics. Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago.

If you've been following developments over the last few years, you may have noticed that deep learning and neural networks have grown wildly. Neural network architecture is able to make predictive judgments in in sports, medicine and the financial sector. For companies looking to predict user patterns or how investments will grow, the ability to mobilize artificial intelligence can save labor and protect investments. For consumers trying to understand the world around them, AI can reveal patterns of human behavior and help to restructure our choices. One major challenge to understanding what impact AI and neural network architecture can have in our lives is the rareness of real-world applications.

At Knowm, we are building a new and exciting type of computer processor to accelerate machine learning (ML) and artificial intelligence applications. The goal of Thermodynamic-RAM (kT-RAM) is to run general ML operations, traditionally deployed to CPUs and GPUs, to a physically-adaptive analog processor based on memristors which unites memory and processing. If you haven't heard yet, we call this new way of computing "AHaH Computing", which stands for Anti-Hebbian and Hebbian Computing, and it provides a universal computing framework for in-memory reconfigurable logic, memory, and ML. While we have shown a long time ago that AHaH Computing is capable of solving problems across many domains of ML, we only recently figured out how to use the kT-RAM instruction set and low precision/noisy memristors to build supervised and unsupervised compositional (deep) ML systems. Our method does not require the propagation of error algorithm (Backprop) and is easy to attain with realistic analog hardware, including but not limited to memristors.

Zen monks have been using a tool called a'koan' for hundreds of years to assist them in reaching enlightenment. These koans are like riddles or stories that can only be solved by letting go of ones narrowing believes and stories about how things should be. Zen students sit in silent meditation and observe how the koan is working on them, slowly transforming their way of looking at the world and revealing a tiny piece of the path to nirvana, that place of no suffering. You might wonder what that has to do with cyber security. With the increased popularity of deep learning and the omni presence of the term artificial intelligence (AI), a lot of security practitioners are tricked into believing that these approaches are the magic silver bullet we have been waiting for to solve all of our cyber security challenges.

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This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is "which algorithm should I use?" Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one and done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems.

Using their DeepMind artificial intelligence (AI), Google's Alphabet AI research lab developed a synthetic speech system called WaveNet back in 2016. The system runs on an artificial neural network that's capable of speech samples at an ostensibly better quality than other technologies like it. The voice of AI is becoming more human-like, so to speak. WaveNet has since been improved to work well enough for Google Assistant across all platforms. Now, WaveNet has gotten even better at sounding more human.

The Chinese government is building a $2 billion (£1.5 billion) artificial intelligence (AI) research park as it looks to become a world leader in the field by 2025, Reuters reports, citing local news agency Xinhua. The AI research park -- to be located in west Beijing -- will reportedly be able to accommodate 400 companies and that are expected to generate 50 billion yuan (£5.6 billion) each year. The park's developer, state-owned Zhongguancun Development Group, is hoping to partner with foreign universities and build a "national-level" AI lab in the area, according to Reuters. It will reportedly aim to attract companies working on big data, biometric identification, deep learning, and cloud computing. Russian president Vladimir Putin believes that in the future, the country that leads in AI could dominate the world, while tech billionaire Elon Musk thinks AI will be the most likely cause of WWIII (although his comments should be taken with a pinch of salt).

Hi, I work as a Research Assistant at Insight Centre for Data Analytics at the DCU, in Dublin. In our lab, we have a reading group session on Fridays, where we discuss about interesting research works in mostly Deep Learning. We have fair enough attendance and a discussion session that follows will be for quite a long time(about an hour). I have found this idea extremely useful to get to know about interesting researches around the world.

Abstract--For homeland and transportation security applications, 2D X-ray explosive detection system (EDS) have been widely used, but they have limitations in recognizing 3D shape of the hidden objects. Among various types of 3D computed tomography (CT) systems to address this issue, this paper is interested in a stationary CT using fixed X-ray sources and detectors. However, due to the limited number of projection views, analytic reconstruction algorithms produce severe streaking artifacts. Inspired by recent success of deep learning approach for sparse view CT reconstruction, here we propose a novel image and sinogram domain deep learning architecture for 3D reconstruction from very sparse view measurement. The algorithm has been tested with the real data from a prototype 9-view dual energy stationary CT EDS carry-on baggage scanner developed by GEMSS Medical Systems, Korea, which confirms the superior reconstruction performance over the existing approaches.