"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.
I remember having written a response that was specifically focused on Andrew Ng's Deep Learning training that was launched with a lot of fanfare in October last year. I have added excepts from my Quora answer here and there and this is me just visiting my own answers based on my year long experience since June 2017 working with CEOs and Chair(wo)men of large enterprises, training about 9000 people in my classical (meaning hands-on workshops where we learn the old fashioned way face-to-face) and interacting with tens of thousands of learners worldwide. I will however be brutally honest about my initial observation of the first 1.5 weeks -- which I went through yesterday with great anticipation and truly enjoyed (still enjoying!), of what I experienced. This may actually not have anything to do with his capabilities or intentions rather it("the dilemma") owes this to latest trend (pretty much close to madness) of packing a deep learning course in a MOOC and try to teach to folks everything in bunch of nutshells. I'll get to that in a minute, but first my quick analysis of who this Deep Learning course / specialization may or may not be for.
For many years, China has been struggling to tackle high pollution levels that are crippling its major cities. Indeed, a recent study by researchers at Chinese Hong Kong University has found that air pollution in the country causes an average of 1.1 million premature deaths each year and costs its economy $38 billion. Now researchers at MIT have discovered that air pollution in China's cities may be contributing to low levels of happiness amongst the country's urban population. In a paper published today in the journal Nature Human Behaviour, a research team led by Siqi Zheng, the Samuel Tak Lee Associate Professor in MIT's Department of Urban Studies and Planning and Center for Real Estate, and the Faculty Director of MIT China Future City Lab, reveals that higher levels of pollution are associated with a decrease in people's happiness levels. The paper also includes co-first author Jianghao Wang of the Chinese Academy of Sciences, Matthew Kahn of the University of Southern California, Cong Sun of the Shanghai University of Finance and Economics, and Xiaonan Zhang of Tsinghua University in Beijing.
Although the z vector is just sampled randomly, our ultimate goal is to create a mapping between the distribution of images and our reference distribution Z, such that each vector in z corresponds to a plausibly real image. As a result, despite being meaningless at first, each particular z ends up corresponding to and encoding properties of the image that it will produce. In their simplest form, transposed convolutions work by learning a filter matrix (for example, 3x3), and multiplying that by the value at each pixel to expand its information outward spatially. Each of the single "pixels" in a 4x4 representation influences the values in a 3x3 patch of output; these patches overlap and sum to create the final "blown out" representation. The visual above, while good for building simplified intuition, is a little misleading, since it makes it look like the values of the enlarged patch have to be spun out of a single piece of information from a single pixel.
It seeks to split its focus between the concepts of machine learning and the associated implementation, but really doesn't do a good job in either. It potentially could be used as a reference tool for various tools, acronyms, jargon, and concepts, but is not a good book to become familiar with the concepts of machine learning and how they compare to'traditional' deterministic software principles. Additionally, the samples and use cases it presents are not well organized in a simple way, making it hard to learn via example (e.g.
Researchers from Princeton (N.J.) University are using machine learning to design a system that could reduce the frequency of tests and improve the timing of critical treatments for intensive care unit patients. To create the system, the researchers used data from more than 6,060 patients admitted to the ICU between 2001 and 2012. The research team presented its results Jan. 6 at the Pacific Symposium on Biocomputing in Hawaii. The analysis looked at four blood tests measuring lactate, creatinine, blood urea nitrogen and white blood cells. These indicators help diagnose two serious problems for ICU patients: kidney failure or sepsis.
Machine learning on graphs is a difficult task due to the highly complex, but also informative graph structure. This post is the second in a series on how to do deep learning on graphs with Graph Convolutional Networks (GCNs), a powerful type of neural network designed to work directly on graphs and leverage their structural information. In my previous post on GCNs, we a saw a simple mathematical framework for expressing propagation in GCNs. In short, given an N F⁰ feature matrix X and a matrix representation of the graph structure, e.g., the N N adjacency matrix A of G, each hidden layer in the GCN can be expressed as Hⁱ f(Hⁱ ¹, A)) where H⁰ X and f is a propagation rule. Each layer Hⁱ corresponds to an N Fⁱ feature matrix where each row is a feature representation of a node.
If you ventured in the North Hall of the recent Consumer Electronic Show (CES) in Las Vegas, you would be mistaken for stepping into a car show. AI was literally everywhere with innovators showcasing how the technology would make everyone's life easier and how it would give us back that most valuable gift – time. That's something that I and every commuter can appreciate with the Auto Insurance Center estimating that the average commuter spends 42 hours a week (a full work week!) in traffic. This is why artificial intelligence and machine learning technologies are viewed so strategically, not only in our daily lives but also in business. When our cars transform from a tool you use to get to a destination to an AI-driven service that delivers you to your destination (self-driving car is your digital chauffeur assistant), you unlock the ability to refocus your time and energy potentially on more high value needs while letting the intelligence in a connected and AI-driven car manage the mundane tasks.
The truth of artificial intelligence is not something of the far-off future. Machine learning is currently transforming our everyday lives and the decisions we make. It is shaping and simplifying the way we live, work, travel and communicate. We focus on 8 industries that can benefit hugely through machine learning in 2019. Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.
In this tutorial a small introduction of machine learning focused on development will be done with one of the most used Java libraries for this purpose, Weka. The machine learning is a subfield of data science . If data science covers the entire process of obtaining knowledge, cleaning, analysis, visualization and data deployment, machine learning are the algorithms and techniques used in the analysis and modeling phase of this process. Within these, we will focus on supervised learning, which is often used for classification and regression problems. The classification can be applied when dealing with a discrete class, where the objective is to predict one of the mutually exclusive values in the target variable.
There are two types of churn -- one at the micro level, between an app and a specific user, and the other at the macro level, between an app and all of its users. And the two influence each other in ways that are not at all intuitive. That's problematic for publishers and developers alike, considering the cash at stake -- $70.3 billion in revenue was generated by mobile games in 2018, according to NewZoom's recent Global Games Market Report, and it is expected to climb to $106.3 billion in 2021. A new method promises to provide greater understanding with the help of machine learning. It's described in a paper published on the preprint server Arxiv.org