This is a Lasso; it is used to pick and capture animals. As a non-native English speaker, my first exposure to this word is in supervised learning. In this LASSO data science tutorial, we discuss the strengths of the Lasso logistic regression by stepping through how to apply this useful statistical method for classification problems in R and how the Lasso can be "similarly" used to pick and select input variables that are relevant to the classification problem at hand. Data analysts and data scientists use different regression methods for different kinds of analytics problems. One of the most talked-about methods is the Lasso.
If you ask any group of data science students about the types of machine learning algorithms, they will answer without hesitation: supervised and unsupervised. However, if we ask that same group to list different types of unsupervised learning, we are likely to get an answer like clustering but not much more. While supervised methods lead the current wave of innovation in areas such as deep learning, there is very little doubt that the future of artificial intelligence(AI) will transition towards more unsupervised forms of learning. In recent years, we have seen a lot of progress on several new forms of unsupervised learning methods that expand way beyond traditional clustering or principal component analysis(PCA) techniques. Today, I would like to explore some of the most prominent new schools of thought in the unsupervised space and their role in the future of AI.
In March 2017, I joined the MathWorks Student Competitions team to focus on supporting university-level robotics competitions. The competition I spend most time with is RoboCup, which is great because RoboCup contains a variety of leagues and skill levels that keeps me sharp with almost everything going on in the field. Today I will talk about my experience in this role, and what it's been like returning to robotics and academia after more than 5 years away from the field. Let me start with a personal history lesson about my experience in robotics. I am a mechanical engineer with a background in controls, dynamics, and systems.
Object detection is a computer vision technique whose aim is to detect objects such as cars, buildings, and human beings, just to mention a few. The objects can generally be identified from either pictures or video feeds. Object detection has been applied widely in video surveillance, self-driving cars, and object/people tracking. In this piece, we'll look at the basics of object detection and review some of the most commonly-used algorithms and a few brand new approaches, as well. Object detection locates the presence of an object in an image and draws a bounding box around that object.
The goal of performance is to provide lightweight tools to assess and check the quality of your model. In this posting, we want to focus on multicollinearity. Multicollinearity "is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others" (source), i.e. two or more predictors are more or less strongly correlated (also described as non-independent covariates). Multicollinearity may lead to severly biased regression coefficients and standard errors. The function works for "simple" models, but also for mixed models, including zero-inflated mixed models fitted with the glmmTMB or GLMMadapative packages.
Image segmentation is an important step in image processing, and it seems everywhere if we want to analyze what's inside the image. For example, if we seek to find if there is a chair or person inside an indoor image, we may need image segmentation to separate objects and analyze each object individually to check what it is. Image segmentation usually serves as the pre-processing before pattern recognition, feature extraction, and compression of the image. Image segmentation is the classification of an image into different groups. Many kinds of research have been done in the area of image segmentation using clustering.
We are living in an age where almost any information is just a swipe away. There are innumerable blogs, videos, articles, papers and even podcasts on any topic that you want to consume information on. There are so many school thoughts in each and a lot of information on each in the internet. You are kinda getting the gist of where I am getting at. There is just so much information out there that it causes analysis paralysis and we become just consumers of information with nothing to act on.
In this series I am going to provide you very brief introduction about time series analysis. Lets explore some basic terms used in time series. It is a increase or decrease of behavior of data over a period of time. It can be linear or non-leaner. If there is upward or increase behavior called as Up-Trend, same for decrease know as Down-Trend.
About the Company: PlaceIQ is a powerful, location-based audience and insights platform that organizes a wide variety of consumer activity data around a precise location base map at massive scale. PlaceIQ uses its detailed understanding of location and consumer activity to reach a targeted audience, and also to derive powerful insights about consumer behavior to inform market and business strategies for national brands. Summary: Having already assembled an exceptionally skilled, diverse and passionate team of developers and data scientists, we are looking for world-class engineers that can (or are willing to learn how to…) do it all. From building data pipelines to regression models/classification algorithms, complex data visualizations to geospatial clustering, a full stack enterprise targeting platform to big data analytics, PlaceIQ software engineers live for huge challenges and know how to deliver in a fast paced, agile environment. Our unique culture breeds excellence and embraces creativity as we look to innovate and drive our business forward.