"Many researchers … speculate that the information-processing abilities of biological neural systems must follow from highly parallel processes operating on representations that are distributed over many neurons. [Artificial neural networks] capture this kind of highly parallel computation based on distributed representations"
– from Machine Learning (Section 4.1.1; page 82) by Tom M. Mitchell, McGraw Hill Companies, Inc. (1997).
It's another graph neural networks survey paper today! Clearly, this covers much of the same territory as we looked at earlier in the week, but when we're lucky enough to get two surveys published in short succession it can add a lot to compare the two different perspectives and sense of what's important. In particular here, Zhou et al., have a different formulation for describing the core GNN problem, and a nice approach to splitting out the various components. Rather than make this a standalone write-up, I'm going to lean heavily on the Graph neural network survey we looked at on Wednesday and try to enrich my understanding starting from there. For this survey, the GNN problem is framed based on the formulation in the original GNN paper, 'The graph neural network model,' Scarselli 2009.
If you've been at machine learning long enough, you know that there is a "no free lunch" principle -- there's no one-size-fits-all algorithm that will help you solve every problem and tackle every dataset. I work for Springboard -- we've put a lot of research into machine learning training and resources. At Springboard, we offer the first online course with a machine learning job guarantee. What helps a lot when confronted with a new problem is to have a primer for what algorithm might be the best fit for certain situations. Here, we talk about different problems and data types and discuss what might be the most effective algorithm to try for each one, along with a resource that can help you implement that particular model.
As part of the MIT Deep Learning series of lectures and GitHub tutorials, we are covering the basics of using neural networks to solve problems in computer vision, natural language processing, games, autonomous driving, robotics, and beyond. This blog post provides an overview of deep learning in 7 architectural paradigms with links to TensorFlow tutorials for each. It accompanies the following lecture on Deep Learning Basics as part of MIT course 6.S094: Deep learning is representation learning: the automated formation of useful representations from data. How we represent the world can make the complex appear simple both to us humans and to the machine learning models we build. My favorite example of the former is the publication in 1543 by Copernicus of the heliocentric model that put the Sun at the center of the "Universe" as opposed to the prior geocentric model that put the Earth at the center.
Algorithms put logic, science and reasoning behind computer machines. Finding best algorithm to use in a specific case is called experience, knowledge and need. You can call it as an another algorithm. You can refer to post "The Exciting Evolution of Machine Learning" for more details on time lines & history on machine learning. Algorithms in Machine Learning (ML) borrows principles from computer science.
Artificial intelligence (AI) was by far the hottest trend discussed in sessions and across the expo floor at the world's largest radiology conference, the 2018 Radiological Society Of North America (RSNA). At the meeting in late November, there was an explosion of AI and deep learning algorithms across the expo floor. How machine learning will impact medical imaging was the key takeaway from the opening session, where examples of how AI will alter medical imaging in the near future were highlighted. Here is an overview of the types of AI software being developed and a few examples from RSNA that are specific to cardiovascular imaging. Artificial intelligence has been a growing topic in past years at RSNA, but this year several companies showed products that recently gained U.S. Food and Drug Administration (FDA) market clearance.
At last year's re:Invent 2018 conference in Las Vegas, Amazon took the wraps off SageMaker Neo, a feature that enabled developers to train machine learning models and deploy them virtually anywhere their hearts desired, either in the cloud or on-premises. It worked as advertised, but the benefits were necessarily limited to AWS customers -- Neo was strictly a closed-source, proprietary affair. Amazon yesterday announced that it's publishing Neo's underlying code under the Apache Software License as Neo-AI and making it freely available in a repository on GitHub. This step, it says, will help usher in "new and independent innovations" on a "wide variety" of hardware platforms, from third-party processor vendors and device manufacturers to deep learning practitioners. "Ordinarily, optimizing a machine learning model for multiple hardware platforms is difficult, because developers need to tune models manually for each platform's hardware and software configuration," Sukwon Kim, senior product manager for AWS Deep Learning, and Vin Sharma, engineering leader, wrote in a blog post.
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection or Anomaly Detection. Since 2017, PyOD has been successfully used in various academic researches and commercial products   . Important Notes: PyOD contains neural network based models, e.g., AutoEncoders, which are implemented in Keras. This reduces the risk of damaging your local copies.
In the last decade, the area of artificial intelligence (AI) has exploded with interesting and promising results. With major achievements in image recognition, speech recognition and highly complex games, AI continues to disrupt society. This blog post will discuss practical applications of AI, optimization and interpretability of deep learning models and reinforcement learning (RL), based on the 2018 REWORK Deep Learning Summit in Toronto. Four software engineers from Knowit had the pleasure of travelling to Canada to attend this conference, and with renowned speakers such as Geoff Hinton attending, it turned out be an insightful experience. Today, the addition of learning is also in place, but due to the non-deterministic nature of the real world, decisions cannot be made purely from the facts that are given. Further development of AI will require improvements in a variety of areas.
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This paper provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. The reader is assumed to be familiar with basic machine learning concepts.