Logistic regression is a part of the supervised learning category; it measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic/sigmoid function. In spite of the name'logistic regression,' this is not used for regression problem where the task is to predict the real-valued output. It is a classification problem which is used to predict a binary outcome (1/0, -1/1, True/False) given a set of independent variables. In linear regression, you predict a real-valued output y based on a weighted sum of input variables as shown below. The aim of linear regression is to estimate values for the model coefficients c, w1, w2, w3 ….wn and fit the training data with minimum error to predict the output y.
The Complete Machine Learning Course in Python has been FULLY UPDATED for November 2019! With brand new sections as well as updated and improved content, you get everything you need to master Machine Learning in one course! Foundations of Deep Learning covering topics such as the difference between classical programming and machine learning, differentiate between machine and deep learning, the building blocks of neural networks, descriptions of tensor and tensor operations, categories of machine learning and advanced concepts such as over- and underfitting, regularization, dropout, validation and testing and much more. Computer Vision in the form of Convolutional Neural Networks covering building the layers, understanding filters / kernels, to advanced topics such as transfer learning, and feature extrations. All the codes have been updated to work with Python 3.6 and 3.7 Get the most up to date machine learning information possible, and get it in a single course!
This post will present a R&D project, done in collaboration with Géolithe, applied to risk assessment in montainous environment. One year ago, Oslandia started to work on a R&D challenge that was organized by Imaginove, a french innovation center (now known as Minalogic). This challenge dealed with country planning in montainous environment, and was part of a bigger project called Univers. The Univers project was set up in order to encourage collaborations between Rhône-Alpes actors involved in digital technologies and geospatial data exploitation. The challenge use case was provided by Géolithe, a french engineering company that focuses on geology, geophysics, geotechnics and civil engineering.
Would you act differently if you knew that somebody constantly watches you to analyze your behavior and emotions? People will have to answer this question soon. While facial recognition and other biometric technologies are becoming more prominent in a range of services including password authentication and Internet of Things (IoT) devices, the pervasiveness of such technologies in our daily lives is becoming disturbing. And this says nothing about how fast they are spreading. Surveillance in retail stores that analyzes the behavior of shoppers, facial-recognition technology to expedite checking in to flights and hotels, targeted-marketing algorithms that deliver personalized ads by scanning the customer's face – these are a few examples, the tip of the iceberg, of how industries are using artificial-intelligence (AI) tech.
In March 2004, the U.S. Defense Advanced Research Projects Agency (DARPA) organized a special Grand Challenge event to test out the promise -- or lack thereof -- of current-generation self-driving cars. Entrants from the world's top A.I. labs competed for a $1 million prize; their custom-built vehicles trying their best to autonomously navigate a 142-mile route through California's Mojave Desert. The "winning" team managed to travel just 7.4 miles in several hours before shuddering to a halt. A decade-and-a-half, a whole lot has changed. Self-driving cars have successfully driven hundreds of thousands of miles on actual roads.