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Posterior Probability

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In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data. This contrasts with the likelihood function, which is represented as P(D H). This distinction is more of an interpretation rather than a mathematical property as both have the form of conditional probability. In order to calculate the posterior probability, we use Bayes theorem, which is discussed below. Bayes theorem, which is the probability of a hypothesis given some prior observable data, relies on the use of likelihood P(D H) alongside the prior P(H) and marginal likelihood P(D) in order to calculate the posterior P(H D).


Serverless' greatest strength is also its greatest weakness - JAXenter

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We're big fans of keeping track of what is going on in the developer community. So, what does the technical world look like today? And more importantly, where is it going? SlashData's Developer Economics global survey reached more than 21,000 developers from around the world and focused on four major themes: AI, serverless, augmented and virtual reality, and programming languages. According to their research, Machine learning and AI are poised to fuel a new wave of innovation.


Logistic Regression with Python

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Logistic regression was once the most popular machine learning algorithm, but the advent of more accurate algorithms for classification such as support vector machines, random forest, and neural networks has induced some machine learning engineers to view logistic regression as obsolete. Though it may have been overshadowed by more advanced methods, its simplicity makes it the ideal algorithm to use as an introduction to the study of machine learning. Like most machine learning algorithms, logistic regression creates a boundary edge between binary labels. The purpose of a training process is to place this edge in such a way that most of the labels are divided so as to maximize the accuracy of predictions. The training process requires correct model architecture and fine-tuned hyperparameters, whereas data play the most significant role in determining the prediction accuracy.


Machine Learning Training Courses – DataMites Training & Certification

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In the world of Big Data, where data comes in high volume, high velocity and variety, making a data driven business decision become very crucial for business success. Machine Learning models plays a significant role with learning algorithms in turn these big data in to valuable insights.


How we use Machine learning at DigitalBridge

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It's difficult to renovate a bathroom. There are a thousand things to do, all of which a typical customer has never done before. This problem is compounded by the imagination gap. When a customer views a product, it can be difficult for them to picture how that product will look in their bathroom. Is there a good place for that product?


8 Useful Industry 4.0 Slides AISOMA AG Frankfurt

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Industry 4.0 is a name given to the current trend of automation and data exchange in manufacturing technologies. It includes cyber-physical systems, the Internet of things, cloud computing and cognitive computing. Industry 4.0 is commonly referred to as the fourth industrial revolution. Industry 4.0 fosters what has been called a "smart factory". Within modular structured smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world and make decentralized decisions.


The Difference Between Big Data and Machine Learning

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Big data and machine learning have become buzzwords we hear thrown around a lot, without necessarily understanding the nuances of each concept. While the two fields certainly aren't mutually exclusive – and in fact intersect in ever more crucial ways – there are some key differences between big data and machine learning that businesses should understand before undertaking a project in either direction.


Data Science Tutorial – Learn Data Science from experts – Intellipaat

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To predict something useful from the datasets, we need to implement machine learning algorithms. Since, there are many types of algorithm like SVM, Bayes, Regression, etc. We will be using four algorithms- Dimensionality Reduction It is a very important algorithm as it is unsupervised i.e. it can implement raw data to structured data.


GSMA Intelligence -- Research -- Infographic: 2019: When 5G becomes a reality

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Whilst every care is taken to ensure the accuracy of the information contained in this material, the facts, estimates and opinions stated are based on information and sources which, while we believe them to be reliable, are not guaranteed. In particular, it should not be relied upon as the sole source of reference in relation to the subject matter. No liability can be accepted by GSMA Intelligence, its directors or employees for any loss occasioned to any person or entity acting or failing to act as a result of anything contained in or omitted from the content of this material, or our conclusions as stated. The findings are GSMA Intelligence's current opinions; they are subject to change without notice. The views expressed may not be the same as those of the GSM Association.


Robot following a walkway with OpenCV and Tensorflow

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After my robot learned how to follow a line, there is a new challenge appeared. I decided to go outdoor and make the robot move along a walkway. It would be nice if a robot follows the host through a park like a dog. The implementation idea was given by Behavioral cloning. It is a very popular approach for self-driving vehicles when AI learns on provided behavioral input and output and then makes decisions on new input.