machine learning


Affinity Matrix

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An Affinity Matrix, also called a Similarity Matrix, is an essential statistical technique used to organize the mutual similarities between a set of data points. Similarity is similar to distance, however, it does not satisfy the properties of a metric, two points that are the same will have a similarity score of 1, whereas computing the metric will result in zero. Typical examples of similarity measures are the cosine similarity and the Jaccard similarity. These similarity measures can be interpreted as the probability that that two points are related.


Machine Learning Building KNN Model Eduonix

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This Video will help you build a KNN model, we will work on a cancel cell Data set, In pattern recognition, the k-nearest neighbors algorithm is a non-parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space Get flat 15% OFF on the above complete course with other projects here with certification - http://bit.ly/2TwTcxh Get 10% flat off on the Below full E-Degree with certification - (APPLY COPOUN - YTDEG) The Best courses to do with Eduonix with are - 1.Learn Machine Learning By Building Projects - http://bit.ly/2MxMSSl 2.The Complete Web Development Course - Build 15 Projects - http://bit.ly/32Ah9oW 3.The Full Stack Web Development - http://bit.ly/2MZDBRV 4.Projects In Laravel: Learn Laravel Building 10 Projects - http://bit.ly/2MAiHtH 5.Mathematical Foundation For Machine Learning and AI - http://bit.ly/2N23Eb1 Get 15% flat off on the below courses with certification - (APPLY COPOUN - YTEDU) Python Programming An Expert Guide on Python - http://bit.ly/2Bp75Dj Get 10% flat off on the Below full E-Degree with certification - (APPLY COPOUN - YTDEG) AI & ML E-degree- http://bit.ly/2mEUCYC


Operations 2.0 is coming – are retailers ready?

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Successful retailers are already modernising their work environments, extracting more value from their data, deepening their understanding of customers, innovating faster and migrating their operations to the cloud – all to become more competitive. While some retailers are focused on improving their existing operations processes, this is not enough. Real value creation and cost reduction comes when retailers reimagine the operations process entirely and take a digital-first strategy, rather than making incremental changes to existing models. This is where artificial intelligence (AI) has a crucial role to play. Cognizant's research indicates that the revenue impact of new technologies, including AI, is $634bn (£483bn) globally.


AI can diagnose breast cancer more accurately than a doctor can

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Artificial intelligence can diagnose breast cancer more accurately than trained doctors, a study suggests. The research on almost 30,000 women who underwent screening found a computer programme could reduce the number of cases missed by more than two thirds. Researchers said the algorithmdeveloped by Imperial College London, Northwestern University in Chicago and Google Health was a "huge advance" in early detection of cancers. Breast cancer is the most common type of cancer in the UK, affecting around one in eight women - with 55,000 diagnoses annually and 11,000 deaths. Experts said the breakthrough could save thousands of lives, by finding deadly tumours that would otherwise go undetected.


9 reasons to be optimistic about tech in 2020

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While this will yield increased profits for companies who can effectively leverage these technologies into new business models, what makes these developments truly revolutionary is their ability to tackle some of the world's most pressing challenges, ranging from education to health. Experts and fellows from the World Economic Forum's Centre for the Fourth Industrial Revolution weigh in with their predictions for the most exciting ways in which new technologies will improve the state of the world in the coming year. When I was born in 1992, I arrived four months premature with every joint in my body bent together as tightly as possible -- from my head being pressed down on my right shoulder all the way down to my toes being pressed against the bottom of my feet and my ankles collapsed against the back of shins like a broken golf club. My twin sister had shared the same environment with me and was 100% healthy. There was only one culprit: a genetic mutation.


Building And Leading A Successful AI Practice In Your Organization

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Artificial intelligence is transforming organizations by eliminating tedious and repetitive processes and empowering organizations to make more data-driven decisions. Yet, many companies struggle to see how to deploy the right AI solution for their organizations. While they may have a general understanding of what AI can do, it remains unclear, specifically, how AI can make organizations more efficient. As an executive working heavily in AI, my hope is that this article helps companies discover whether AI is the right fit for their organizations' unique needs, and if so, how to launch an AI practice that delivers continuous business value. It outlines the benefits of AI for the enterprise, provides a list of questions organizations can ask themselves before getting started.


Coding Dopamine: DeepMind Brings AI To The Footsteps Of Neuroscience

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DeepMind has been trying to bridge the gap between AI and biology for quite some time now. All their endeavours revolve around solving the problem of intelligence in machines. The straightforward trivial tasks for humans can be very, very sophisticated and almost for devices. While human brains are hardcoded with millions of years of learning, the machines have many limitations when it comes to data. They can be fed with data that has been documented or prepared by humans, the magnitude of which is historically insignificant when compared to humans.


Full stack web dev, machine learning and AI integrations

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This extensive course leads you through a complete range of software skills and languages, skilling you up to be an incredibly on-demand developer. The combination of being able to create full-stack websites AND machine learning and AI models is very rare - something referred to as a unAIcorn. This is exactly what you will be able to do by the end of this course. Whether you're looking to get into a high paying job in tech, aspiring to build a portfolio so that you can land remote contracts and work from the beach, or you're looking to grow your own tech start-up, this course will be essential to set you up with the skills and knowledge to develop you into a unAIcorn. It won't matter if you're a complete beginner to software or a seasoned veteran.


Computer Vision and Image Analytics

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Over the past few months, I've been working on a fascinating project with one of the world's largest pharmaceutical companies to apply SAS Viya computer vision to help identify potential quality issues on the production line as part of the validated inspection process. As I know the application of these types of AI and ML techniques are of real interest to many high-tech manufacturing organisations as part of their Manufacturing 4.0 initiatives, I thought I'd take the to opportunity to share my experiences with a wide audience, so I hope you enjoy this blog post. For obvious reasons, I can't share specifics of the organisation or product, so please don't ask me to. But I hope you find this article interesting and informative, and if you would like to know more about the techniques then please feel free to contact me. Quality inspections are a key part of the manufacturing process, and while many of these inspections can be automated using a range of techniques, tests and measurements, some issues are still best identified by the human eye.


Implementing a fully convolutional network (FCN) in TensorFlow 2

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Using a pre-trained model that is trained on huge datasets like ImageNet, COCO, etc. we can quickly specialize these architectures to work for our unique dataset. This process is termed as transfer learning. Pre-trained models for image classification and object detection tasks are usually trained on fixed input image sizes. These typically range from 224x224x3 to somewhere around 512x512x3 and mostly have an aspect ratio of 1 i.e. the width and height of the image are equal. If they are not equal then the images are resized to be of equal height and width.