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What Is SVM?

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Support Vector Machine (SVM) is an approach for classification which uses the concept of separating hyperplane. It was developed in the 1990s. It is a generalization of an intuitive and simple classifier called maximal margin classifier. In order to study Support Vector Machine (SVM), we first need to understand what is maximal margin classifier and support vector classifier. In maximal margin classifier, we use a hyperplane to separate the classes.


Convolutional Neural Networks, Explained

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A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. A digital image is a binary representation of visual data. It contains a series of pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be. The human brain processes a huge amount of information the second we see an image. Each neuron works in its own receptive field and is connected to other neurons in a way that they cover the entire visual field.


Support Vector Machines (SVM) and its Python implementation

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The support vector machines algorithm is a supervised machine learning algorithm that can be used for both classification and regression. In this article, we will be discussing certain parameters concerning the support vector machines and try to understand this algorithm in detail. For understanding, let us consider the SVM used for classification. The following figure shows the geometrical representation of the SVM classification. After taking a look at the above diagram you might notice that the SVM classifies the data a bit differently as compared to the other algorithms.


Coding Convolutional Neural Networks (CNN) with TensorFlow

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CNNs are one of the state of the art, Artificial Neural Network design architecture, with one of the best deep learning tools in areas such as image recognition and classification. The Basic Principle behind the working of CNN is the idea of Convolution, producing filtered Feature Maps stacked over each other. We'll be using MNIST dataset which is readily available in different libraries. Code has been written in a generic template so as to do very minimal modifications and can run on many datasets with very little change. Every CNN is made up of multiple layers, the three main types of layers are convolutional, pooling, and fully-connected.


Automatic Music Generation

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"If I were not a physicist, I would probably be a musician. I often think in music. I live my daydreams in music. I see my life in terms of music." I might not be a physicist like Mr. Einstein, but I wholeheartedly agree with his thoughts on music!


Convolution Filters

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Convolution Filters (also known as kernels) are used with images for blurring, sharpening, embossing, edge detection, and more. This is accomplished by doing a convolution between a kernel and an image. Where g(x,y) is the output filtered image, f(x,y) is the original image and w is the filter kernel. To explain how this works here's some example pixels from the top left-hand corner of an image. We'll apply the convolution kernel to the value in the pixel at location (1,1): The filter is taking values from around the pixel of interest -- from locations (x-1, y-1) to (x 1, y 1).


Cadence Delivers Machine Learning up to 5X Faster Regressions – IAM Network

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Core engine performance enhancements accelerate verification throughput by reducing stimulation cycles with matching coverage on randomized test suites. Cadence Design Systems, Inc. today announced that the Cadence Xcelium Logic Simulator has been enhanced with machine learning technology (ML), called Xcelium ML, to increase verification throughput.Using new machine learning technology and core computational software, Xcelium ML enables up to 5X faster verification closure on randomized regressions. Using computational software and a proprietary machine learning technology that directly interfaces to the simulation kernel, Xcelium ML learns iteratively over an entire simulation regression.It analyzes patterns hidden in the verification environment and guides the Xcelium randomization kernel on subsequent regression runs to achieve matching coverage with reduced simulation cycles. Cadence's Xcelium Logic Simulator provides best-in-class core engine performance for SystemVerilog, VHDL, mixed-signal, low power, and x-propagation.It supports both single-core and multi-core simulation, incremental and parallel build, and save/restart with dynamic test reload. The Xcelium Logic Simulator has been deployed by a majority of top semiconductor companies, and a majority of top companies in the hyper-scale, automotive, and consumer electronics segments.Kioxia has effectively utilized Xcelium simulation for a variety of our designs, and it addresses our ever-growing verification needs.


SVM : Support vector machine

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The support vector machine is base on the idea of finding the best line or hyperplane that distinctly classifies the data point. SVM can find the best hyperplane in N- dimensions. Here N is the number of features. For example, if you have two features: A, B then the hyperplane is just a line and if there is three features: A, B, and C, your points will be plotted in the corresponding three-dimensional space based on their values for each independent variable. Support vector machine is a powerful supervised machine learning algorithm.


Kite brings its AI-powered code completions to Jupyter notebooks

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Kite, which suggests code snippets for developers in real time, today debuted integration with JupyterLab and support for teams using JupyterHub. Data scientists can now get code completions powered by Kite's deep learning, which is trained on over 25 million open-source Python files, as they type in Jupyter notebooks. Using AI to help developers is not an original idea. Nowadays you have startups like DeepCode offering AI-powered code reviews and tech giants like Microsoft working on applying AI to the entire application developer cycle. But Kite stands out with 250,000 monthly developers using its AI-powered developer environment. Kite has been paving the way since its private debut in April 2016, before launching its developer sidekick powered by the cloud publicly in March 2017.


Image-scaling attacks highlight dangers of adversarial machine learning

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This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. We usually don't expect the image of a teacup to turn into a cat when we zoom out. But in the world of artificial intelligence research, strange things can happen. Researchers at Germany's Technische Universität Braunschweig have shown that carefully modifying the pixel values of digital photos can turn them into a completely different image when they are downscaled. What's concerning is the implications these modifications can have for AI algorithms.