"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.
Out of all the machine learning algorithms I have come across, KNN has easily been the simplest to pick up. Despite it's simplicity, it has proven to be incredibly effective at certain tasks (as you will see in this article). It can be used for both classification and regression problems! It's far more popularly used for classification problems, however. I have seldom seen KNN being implemented on any regression task.
With the help of a five-year, $2.7 million grant from the National Institute of Mental Health, researchers at Vanderbilt University Medical Center will use computational methods to shed light on suicidal ideation and its relationship to attempted suicide, predict suicidal ideation and suicide attempt using routine electronic health records (EHRs) and explore the genetic underpinnings of both. From 1999 to 2017, the all-ages suicide rate in the United States increased 33%, from 10.5 to 14.0 per 100,000 population. In 2017 there were 47,173 recorded suicides, making it the nation's 10th leading cause of death. The principal investigators for the study are internist and clinical informatician Colin Walsh, MD, MA, assistant professor of Biomedical Informatics, Medicine, and Psychiatry and Behavioral Sciences, and geneticist and computational biologist Douglas Ruderfer, PhD, MS, assistant professor of Medicine, Psychiatry and Behavioral Sciences, and Biomedical Informatics. In previous work Walsh and colleagues used EHR data and machine learning techniques to develop predictive algorithms for attempted suicide.
Mostly, you are given a model which has been created by years of engineering and expertise and you cannot change its architecture nor can you retrain it. So how to do you go about interpreting a model about which you have no clue? TCAV is a technique which aims to handle such scenarios. Most Machine Learning models are designed to operate on low-level features like edges and lines in a picture or say the colour of a single pixel. This is very different from the high-level concepts more familiar to humans like stripes in a zebra.
In this tutorial, we show you how to configure TensorFlow with Keras on a computer and build a simple linear regression model. If you have access to a modern NVIDIA graphics card (GPU), you can enable tensorflow-gpu to take advantage of the parallel processing afforded by CUDA. The field of Artificial Intelligence (AI) has been around for quite some time. As we move to build an understanding and use cases for Edge AI, we first need to understand some of the popular frameworks for building machine learning models on personal computers (and servers!). These models can then be deployed to edge devices, such as single-board computers (like the Raspberry Pi) and microcontrollers.
Voleon Group, one of the best known machine-learning hedge funds, returned 7% last year in its flagship strategy after drawing inflows on the back of a stellar performance in 2018. The Berkeley, California-based firm now oversees $6.5 billion overall compared with $5.1 billion in mid-2019, according to people familiar with the matter who asked not to be identified because the information is private. Voleon's Investors Fund gained 14% in 2018, when many of its competitors were hit by the global market tumult that saw the S&P 500 Index drop 6.2%. The group is one of the few systematic players to have built a reputation on strategies run exclusively by artificial intelligence. While proponents say machine-learning can detect multifaceted links between economic forces and security prices, most quants are still struggling to apply the technology to complex financial markets.
Artificial intelligence is here to stay, but as with any helpful new tool, there are notable flaws and consequences to blindly adapting it. From the esoteric worlds of predictive health care and cybersecurity to Google's e-mail completion and translation apps, the impacts of AI are increasingly being felt in our everyday lived experience. The way it has crepted into our lives in such diverse ways and its proficiency in low-level knowledge shows that AI is here to stay. But like any helpful new tool, there are notable flaws and consequences to blindly adapting it. AI is a tool--not a cure-all to modern problems.
Recently, human being's curiosity has been expanded from the land to the sky and the sea. Besides sending people to explore the ocean and outer space, robots are designed for some tasks dangerous for living creatures. Take the ocean exploration for an example. There are many projects or competitions on the design of Autonomous Underwater Vehicle (AUV) which attracted many interests. Authors of this article have learned the necessity of platform upgrade from a previous AUV design project, and would like to share the experience of one task extension in the area of fish detection. Because most of the embedded systems have been improved by fast growing computing and sensing technologies, which makes them possible to incorporate more and more complicated algorithms. In an AUV, after acquiring surrounding information from sensors, how to perceive and analyse corresponding information for better judgement is one of the challenges. The processing procedure can mimic human being's learning routines. An advanced system with more computing power can facilitate deep learning feature, which exploit many neural network algorithms to simulate human brains. In this paper, a convolutional neural network (CNN) based fish detection method was proposed.
Artificial intelligence has been used to quickly and accurately model the 3D flow of light around arbitrarily shaped nanoparticles. Peter Wiecha and Otto Muskens at the University of Southampton in the UK demonstrated the modelling approach using a neural network that required just a single training procedure. Their technique could be used to design a wide range of optical devices that control the paths taken by light. When light interacts with nanostructures that are smaller in size than the wavelength of the light, the result can be very different from how light interacts with larger structures and continuous media. The field of nanophotonics seeks to exploit this by designing nanoparticles with particular shapes and compositions with the aim of manipulating light in specific ways.
With the release of ML.NET, a API that C# developers can use to infuse their applications with machine learning capability, I've been keen to combine my knowledge of Azure Functions with the API to build some wacky serverless machine learning applications that would allow me to enhance my GitHub profile and cater to all the buzzword enthusiasts out there! This post won't be a tutorial. I'm writing this more as a retrospective of the design decisions I took while building the application and the things I learnt about how different components work. Should you read this and decide to build upon it for your real world applications, hopefully you can apply what I've learnt in your projects or better yet, expand on the ideas and scenarios I was working with. I'll be focusing more on what I learnt about the ML.NET API itself rather than spending too much time about how Azure Functions work.
Spaceborne precipitation observing systems can provide global coverage but estimates typically suffer from uncertainties and biases. Conversely, ground based systems such as rain gauges and precipitation radar have higher accuracy but only limited spatial coverage. Chen et al.  have developed a novel deep learning algorithm designed to construct a hybrid rainfall estimation system, where the ground radar is used to bridge the scale gaps between (accurate) rain gauge measurements and (less accurate) satellite observations. Such a non-parametric deep learning technique shows the potential for regional and global rainfall mapping and can also be expanded as a data fusion platform through incorporation of additional precipitation estimates such as outputs of numerical weather prediction models.