Information Technology


Deep learning Data Sets for Every Data Scientist

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Machine Learning has seen a tremendous rise in the last decade, and one of its sub-fields which has contributed largely to its growth is Deep Learning. The large volumes of data and the huge computation power that modern system possess has given Data Scientist, Machine Learning Engineers, and others to achieve ground-breaking results in the Deep Learning and continue to bring in new developments in this field. In this blog post, we would cover the deep learning data sets that you could work with as a Data Scientist but before that, we would provide an intuition about the concept of Deep Learning. A sub-field of Machine Learning, the working structure of Deep Learning is similar to our brain known as the Artificial Neural Networks. It is similar to our nervous system where each neuron connected to each other.


Which type of ML tell us diapers and beer often sale together?

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Already heard talking about Machine Learning in conferences, meetups or in my first article and want to learn more? You're in the right place, the second step for you will be to discover the different kind of machine learning. I'll introduce them to you through many practical examples. ML comes in two flavors: supervised and unsupervised learning. The one you'll choose only depends on your purpose.


Prediction of a plant intracellular metabolite content class using image-based deep learning

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Plant-derived secondary metabolites play a vital role in the food, pharmaceutical, agrochemical and cosmetic industry. Metabolite concentrations are measured after extraction, biochemistry and analyses, requiring time, access to expensive equipment, reagents and specialized skills. Additionally, metabolite concentration often varies widely among plants, even within a small area. A quick method to estimate the metabolite concentration class (high or low) will significantly help in selecting trees yielding high metabolites for the metabolite production process. Here, we demonstrate a deep learning approach to estimate the concentration class of an intracellular metabolite, azadirachtin, using models built with images of leaves and fruits collected from randomly selected Azadirachta indica (neem) trees in an area spanning 500,000 sqkms and their corresponding biochemically measured metabolite concentrations.


A Tic Tac Toe AI with Neural Networks and Machine Learning

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This article is my entry for CodeProject's AI competition "Image Classification Challenge"[ ]. My goal was to teach a neural network to play a game of tic tac toe, starting from only knowing the rules. Tic tac toe is a solved game. A perfect strategy[ ] exists so a neural network is a bit overkill and will not perform as well as existing programs and humans can. Described from a high level: when the AI needs to make a move, it iterates over all possible moves, generates the board after making a given move, and uses the neural network to see how good the position is after performing that move.


Chemical Patterns May Predict Stars That Host Giant Planets - Eos

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Does this star have a planet? A new algorithm could help astronomers predict, on the basis of a star's chemical fingerprint, whether that star will host a giant gaseous exoplanet. "It's like Netflix," Natalie Hinkel, a planetary astrophysicist at the Southwest Research Institute in San Antonio, Texas, told Eos. Netflix "sees that you like goofy comedy, science fiction, and kung fu movies--a variety of different patterns" to predict whether you'll like a new movie. Likewise, her team's machine learning algorithm "will learn which elements are influential in deciding whether or not a star has a planet."


7 Steps to Mastering Data Preparation for Machine Learning with Python -- 2019 Edition

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Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities. Data cleansing may be performed interactively with data wrangling tools, or as batch processing through scripting. This may include further munging, data visualization, data aggregation, training a statistical model, as well as many other potential uses. Data munging as a process typically follows a set of general steps which begin with extracting the data in a raw form from the data source, "munging" the raw data using algorithms (e.g. I would say that it is "identifying incomplete, incorrect, inaccurate or irrelevant parts of the data and then replacing, modifying, or deleting the dirty or coarse data" in the context of "mapping data from one'raw' form into another..." all the way up to "training a statistical model" which I like to think of data preparation as encompassing, or "everything from data sourcing right up to, but not including, model building."


Defending Against Adversarial Examples with K-Nearest Neighbor

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Robustness is an increasingly important property of machine learning models as they become more and more prevalent. We propose a defense against adversarial examples based on a k-nearest neighbor (kNN) on the intermediate activation of neural networks. With our models, the mean perturbation norm required to fool our MNIST model is 3.07 and 2.30 on CIFAR-10. Additionally, we propose a simple certifiable lower bound on the l2-norm of the adversarial perturbation using a more specific version of our scheme, a 1-NN on representations learned by a Lipschitz network. Our model provides a nontrivial average lower bound of the perturbation norm, comparable to other schemes on MNIST with similar clean accuracy.


Defending Against Adversarial Examples with K-Nearest Neighbor

#artificialintelligence

Robustness is an increasingly important property of machine learning models as they become more and more prevalent. We propose a defense against adversarial examples based on a k-nearest neighbor (kNN) on the intermediate activation of neural networks. With our models, the mean perturbation norm required to fool our MNIST model is 3.07 and 2.30 on CIFAR-10. Additionally, we propose a simple certifiable lower bound on the l2-norm of the adversarial perturbation using a more specific version of our scheme, a 1-NN on representations learned by a Lipschitz network. Our model provides a nontrivial average lower bound of the perturbation norm, comparable to other schemes on MNIST with similar clean accuracy.


ADV Webinar: The Impact of Machine Learning on the Enterprise Today - DATAVERSITY

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Despite the dramatic changes we have seen in business recently, another level of change looms. We are headed toward a future permeated with artificial intelligence and machine learning (ML), where machines take on more of the work people have traditionally done, and then some. The potential for ML is enormous. We are at the dawn of a whole new era of intelligent devices that will revolutionize our business and personal worlds. Corporations wishing to lead with AI/ML should make plans now to establish their initiatives and their technology framework and nurture the necessary skills.


Artificial intelligence

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Our Bionic Factory is a centre of expertise focused on AI, data engineering and intelligent automation. This centre will help you tap into additional forms of capital (Behavioral and Cognitive) as you move towards being a Bionic Organization and outperform others that rely solely on more traditional types of capital (Financial, Natural Resources, Human). SCALE.AI is a super cluster dedicated to building the next-generation supply chain and boosting industry performance through AI technologies. Close to 120 partners, including PwC Canada, have joined forces to create this Canadian innovation consortium.