neural network


Machine Learning in Business Intelligence

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Every Business rely on data nowadays to analyze the fundamental information. They use these data to understand the current business performance and find out their past performance trends. This will help businesses to make important business decisions and also help them improve their revenue growth and profits by implementing best practises and key decisions. Not sure how many of you know about the following. I will give you a glimpse on these topics as these are the basics that one should know.


12 Artificial Intelligence (AI) Milestones: 3. Computer Graphics Give Birth To Big Data

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The explosion of breakthroughs, investments, and entrepreneurial activity around artificial intelligence over the last decade has been driven exclusively by deep learning, a sophisticated statistical analysis technique for finding hidden patterns in large quantities of data. A term coined in 1955--artificial intelligence--was applied (or mis-applied) to deep learning, a more advanced version of an approach to training computers to perform certain tasks--machine learning--a term coined in 1959. The recent success of deep learning is the result of the increased availability of lots of data (big data) and the advent of Graphics Processing Units (GPUs), significantly increasing the breadth and depth of the data used for training computers and reducing the time required for training deep learning algorithms. The technology that animated movies like "Toy Story" and enabled a variety of special effects is the ... [ ] focus of this year's Turing Award, the technology industry's version of the Nobel Prize. The term "big data" first appeared in computer science literature in an October 1997 article by Michael Cox and David Ellsworth, "Application-controlled demand paging for out-of-core visualization," published in the Proceedings of the IEEE 8th conference on Visualization.


5 Things to Consider Before Getting Started as a Developer in AI

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Such technology as AI existed for years yet experienced a significant popularity boost just a few years ago. Today, it's far from being a futuristic thing used by the evil geniuses to conquer the planet. Instead, Artificial Intelligence became a cutting-edge tool to improve the lives of millions by assisting us with our job, studies, home duties, and even relationships. There's no need to mention that AI start-ups are the ones to run the IT industry: $37.5 billion was spent on the start-ups somehow involving this technology in 2019. No wonder you want to get on board and become an AI developer.


How Governments Have Used AI to Fight COVID-19

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Anton Dolgikh leads AI and ML-oriented projects in the Healthcare and Life Sciences practice at DataArt and runs educational and training developers focused on solving business problems with ML methods. Prior to working at DataArt, Dolgikh worked in the Department of Complex Systems at the Université Libre de Bruxelles, a leading Belgian private research university. What was it that originally inspired you to pursue AI and life sciences as a career? I always like to read. At university, I discovered a new source of information – articles.


Python Engineer

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KNN (K Nearest Neighbors) in Python - Machine Learning From Scratch 01 - Python Tutorial 7,679 views 6 months ago In this Machine Learning from Scratch Tutorial, we are going to implement the K Nearest Neighbors (KNN) algorithm, using only built-in Python modules and numpy. We will also learn about the concept and the math behind this popular ML algorithm. If you enjoyed this video, please subscribe to the channel! The code can be found here: https://github.com/python-e...... You can find me here: Website: https://www.python-engineer... Twitter: https://twitter.com/python_... GitHub: https://github.com/python-e... #Python #MachineLearning Show less Read more Uploads Play all Complete FREE Study Guide for Machine Learning and Deep Learning - Duration: 12 minutes, 21 seconds.


Recipe for neuromorphic processing systems?

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IMAGE: Like any recipe, an ideal memristive neuromorphic computing system requires a special blend of CMOS circuits and memristive devices, as well as spatial resources and temporal dynamics that must be... view more WASHINGTON, March 24, 2020 -- During the 1990s, Carver Mead and colleagues combined basic research in neuroscience with elegant analog circuit design in electronic engineering. This pioneering work on neuromorphic electronic circuits inspired researchers in Germany and Switzerland to explore the possibility of reproducing the physics of real neural circuits by using the physics of silicon. The field of "brain-mimicking" neuromorphic electronics shows great potential not only for basic research but also for commercial exploitation of always-on edge computing and "internet of things" applications. In Applied Physics Letters, from AIP Publishing, Elisabetta Chicca, from Bielefeld University, and Giacomo Indiveri, from the University of Zurich and ETH Zurich, present their work to understand how neural processing systems in biology carry out computation, as well as a recipe to reproduce these computing principles in mixed signal analog/digital electronics and novel materials. One of the most distinctive computational features of neural networks is learning, so Chicca and Indiveri are particularly interested in reproducing the adaptive and plastic properties of real synapses.


I'm out of the layers -- how to make a custom TensorFlow 2 layer.

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TensorFlow 2 made the machine learning framework far easier to use, still retaining its flexibility to build its models. One of its new features is building new layers through integrated Keras API and easily debugging this API with the usage of eager-execution. In this article, you will learn how to build custom neural network layers in TensorFlow 2 framework. Writing this article I assume you have a basic understanding of object-oriented programming in Python 3. The best would be if you review __init__, __call__, class inheritance and method overriding before reading this article. Let's start from a template, based on it you will build most of your layers.


Addressing Drawbacks Of AutoML With AutoML-Zero

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Automated machine learning – or AutoML – is an approach that cuts down the time spent in doing iterative tasks concerning model development. AutoML tools help developers build scalable models with great ease and minimal domain expertise. AutoML is one of the most actively researched spaces in the ML community. AutoML studies have discovered ways to constrain search spaces to isolated algorithmic aspects. This includes the learning rule used during backpropagation, the gating structure of an LSTM, or the data augmentation.


End to End Chatbot using Sequence to Sequence Architecture

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Ever felt bored when you are all alone? Had a thought of talking to someone who could give you witty replies? If that is the case why not train one to be? I mean a deep learning model. Yes, since the past half-decade deep learning has grown humongously powerful with evolution of state-of-the-art architectures and algorithms that were brought up into the limelight as part of tons of research that's happening around the world.


Machine Learning Framework Algorithm to recognise handwriting

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Manually transcribing large amounts of handwritten data is an arduous process that's bound to be fraught with errors. Automated handwriting recognition can drastically cut down on the time required to transcribe large volumes of text, and also serve as a framework for developing future applications of machine learning. Handwritten character recognition is an ongoing field of research encompassing artificial intelligence, computer vision, and pattern recognition. An algorithm that performs handwriting recognition can acquire and detect characteristics from pictures, touch-screen devices and convert them to a machine-readable form. There are two basic types of handwriting recognition systems – online and offline.