OBSERVATION


Machine Learning as a Service – MLaaS

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To help fill the information gap on feature engineering, MLaaS hands-on can help and support beginning-to-intermediate data scientists how to work with this widely practiced phenomena. Explaining or gaining common practices and mathematical principles to help engineer features for new data and tasks. MLaaS these days provides full automation of essential, yet time-consuming activities in predictive model construction, such as fast variable selection, variable interaction modeling, and variable transformations or best model selection. Conclusion – At end and at heart we all now the dirty secret no matter how good the algorithm is, no matter how good I as data scientist, no model can perform magic if direction, intension, time and goal is not set.


Variance, Clustering, and Density Estimation Revisited

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In some cases, it could be just 0 or 1, with 1 meaning that there is a training set data point close to the location in question in the grid, 0 meaning that you are far enough away from any neighbor. To compute density estimates on each cell of the grid, draw a 3x3 window around each yellow cell, and add 1 to all locations (cells) in that 3x3 window. In our example, the number of groups (g 3: low risk, medium risk, high risk of default) will be multiplied by 2 (M / F) x 3 (young / medium / old), resulting in 18 groups, for instance "young females with medium risk of default" being one of these 18 groups. Of course, accessing a cell in the grid (represented by a 2-dim array), while extremely fast and not depending on the number of observations, still requires a tiny bit of time, but it is entirely dependent only on the size of the array, and its dimension.


State-of-the-Art Machine Learning Automation with HDT

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In its simplest form, our particular problem consists of analyzing historical data about articles and blog posts, to identify features (also called metrics or variables) that are good predictors of blog popularity when combined together, to build a system that can predict the popularity of an article before it gets published. As we have seen in the previous section, the problem consists of predicting pv, the logarithm of unique page views for an article (over some time period), as a function of keywords found in the title, and whether the article in question is a blog or not. Some nodes have a far larger volatility, for instance when one of the keywords has different meanings, such as the word "training", in "training deep learning" (training set) versus "deep learning training" (courses.) It involves training sets, cross-validation, feature selection, binning, and populating hash tables of key-value pairs (referred to here as the nodes).


machine-learning-algorithms?utm_content=bufferfa124&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

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Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Classification is the supervised learning task for modeling and predicting categorical variables. Support vector machines (SVM) use a mechanism called kernels, which essentially calculate distance between two observations. We've just taken a whirlwind tour through modern algorithms for the "Big 3" machine learning tasks: Regression, Classification, and Clustering.


Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms

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Once these data subsets are created from the primary dataset, a predictive model or classifier is trained using the training data, and then the model's predictive accuracy is determined using the test data. As mentioned, machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques. The columns in this case, and the data contained in each, represent the features (values) of the data, and may include feature data such as game date, game opponent, season wins, season losses, season ending divisional position, post-season berth (Y/N), post-season stats, and perhaps stats specific to the three phases of the game: offense, defense, and special teams.


Teaching machines to understand video could be the key to giving them common sense

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Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.


Teaching machines to understand video could be the key to giving them common sense

#artificialintelligence

Five years ago, researchers made a sudden leap in the accuracy of software that can interpret images. The technology behind it, artificial neural networks, underpins the recent boom in artificial intelligence (see "10 Breakthrough Technologies 2013: Deep Learning"). Yann LeCun, director of Facebook's AI research group and a professor at New York University, helped pioneer the use of neural networks for machine vision. That's what would allow them to acquire common sense, in the end.


Artificial General Intelligence – The Holy Grail of AI

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Summary: Artificial General Intelligence (AGI) is the long sought after'brain' that brings together all the branches of AI into a general purpose platform that can perform with human level intelligence in a broad variety of tasks. For starters, the incremental gains in partial AI represented by deep learning and robotics have bigger money and bigger companies behind them and more close-in applications. However, it's unlikely we could find a single human able to master every single economically important job so perhaps we can declare victory when the AGI can master one or more jobs if not all. Inductive reasoning makes broad generalizations from specific observations.


A Primer in Adversarial Machine Learning – The Next Advance in AI

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Even more concerning, researchers have shown that completely random nonsense images can be misclassified by CNNs with very high confidence as objects recognizable to humans, even though a human would clearly recognize that there was no image there at all (e.g. If those system observations are intentionally tainted with noise designed to defeat the CNN recognition, the system will be trained to make incorrect conclusions about whether a malevolent intrusion is occurring. Adversarial Machine Learning is an emerging area in deep neural net (DNN) research. The current state of AI has advanced to general image, text, and speech recognition, and tasks like steering the car or winning a game of chess.


Machine Learning: An In-Depth Guide - Overview, Goals, Learning Types, and Algorithms

#artificialintelligence

Once these data subsets are created from the primary dataset, a predictive model or classifier is trained using the training data, and then the model's predictive accuracy is determined using the test data. As mentioned, machine learning leverages algorithms to automatically model and find patterns in data, usually with the goal of predicting some target output or response. In a nutshell, machine learning is all about automatically learning a highly accurate predictive or classifier model, or finding unknown patterns in data, by leveraging learning algorithms and optimization techniques. The columns in this case, and the data contained in each, represent the features (values) of the data, and may include feature data such as game date, game opponent, season wins, season losses, season ending divisional position, post-season berth (Y/N), post-season stats, and perhaps stats specific to the three phases of the game: offense, defense, and special teams.