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Understanding Machine Learning - DZone Big Data

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Branch of AI: Artificial intelligence is the study and development by which a computer and its systems are given the ability to successfully accomplish tasks that would typically require a human's intelligent behavior. Supervised learning: in this type of learning, the correct outcome for each data point is explicitly labeled when training the model. In a classification context, the learning algorithm could be, for example, fed with historic credit card transactions each labeled as safe or suspicious. Machine learning is used to find meaningful relations and to predict outcomes while data experts serve as translators to make sense of why the relation exists.


How Machine Learning, Big Data And AI Are Changing Healthcare Forever

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While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Lumiata has developed predictive analytics tools that can discover accurate insights and make predictions related to symptoms, diagnoses, procedures, and medications for individual patients or patient groups. The Care Trio team has developed a three-pronged approach that helps doctors devise and understand the best care protocols for cancer patients.


deep-learning-startup-clarifai-raises-30-million

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Clarifai, a startup providing an application programming interface (API) that offers a type of artificial intelligence (A.I.) known as "deep learning," is announcing a $30 million round of funding today. Beyond its core application programming interface (API) for image and video recognition, Clarifai has launched the Forevery photo storage app for iOS and recently introduced Custom Training and Visual Search services. To date, Clarifai has raised $41.25 million, including the $10 million round from last year.


fundamentals-of-machine-learning-for-predictive-data-analytics-algorithms-worked-examples-and-case-studies-mit-press-2

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This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.


The Journal of Open Source Software

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Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to time-consuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We've designed Osprey to provide scientists with a practical, easy-to-use way of finding optimal model parameters. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times.


Quick Pro Quo: Software Writes Text 3x Faster Than Any Human Can

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Baidu's Deep Speech 2 is a cloud-based voice recognition software based on a deep learning neural network. "But we were noticing that in the past two to three years, speech recognition was actually improving a lot, benefiting from big data and deep learning to train its neural networks to produce faster, more accurate results. For English, the speech recognition software was three times faster with a 20.4 percent low error rate. For Mandarin Chinese, the software was 2.8 times faster with a 63.4 percent lower error rate compared to typing.


Why Big Tech Companies Are Open-Sourcing Their AI Systems

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The traditional approach to science involves collecting data, analyzing the data and publishing the findings in a paper. Microsoft, Google, Facebook and Amazon have been making remarkable progress developing artificial intelligence systems. Neither the motivations of DARPA nor OpenAI explain exactly why these commercial technology companies are open sourcing their AI code. Open-sourcing AI serves these companies' broader goals of staying at the cutting edge of technology.


Want to tap machine learning like Google? There's an app for that

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Google claimed that TensorFlow's distributed architecture gives it a high level of flexibility in how coders define models that train the software. "To make TensorFlow easier to use, we have included Python libraries that make it easy to write a model that runs on a single process and scales to use multiple replicas for training".Distributed computing allows neural networks to learn much faster than the network running on one computer. Engineering leader of TensorFlow Rajat Monga said the reason why TensorFlow's multi-server version was delayed for release because they found it hard to adapt the open-source software to be usable outside of the highly customized data centers of Google. But for many researchers, its expense might as well place it in outer space.TensorFlow comes in a branch of artificial intelligence called deep learning, it works the same way human brain cells interact together.Equally, having access to the combined power of even a small cluster of computers, rather than relying on one machine, means that the overall data throughput of machine learning models and the speed at which they deliver accurate results can be accelerated.Regardless of the advanced feature, TensorFlow has already gained popularity for its software.The Verge has a report covering some of the more compelling projects that developers have created using TensorFlow.


David R. Cheriton School of Computer Science, University of Waterloo – Canada

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The Cheriton School is part of Waterloo's highly regarded and unique Faculty of Mathematics, which also placed in the top 20 in the 2015 QS rankings With those credentials, it is little wonder that the Faculty has more than 7,500 graduate and undergraduate students. "At the University of Waterloo, we build innovative, high-impact platforms, systems, and applications for tackling the big data challenge," says Prof. Jimmy Lin. To produce globally influential technology leaders, the Cheriton School has formed 16 different research groups of professors and graduate students, who explore innovations in a myriad of areas such as human computer interaction, machine learning and artificial intelligence, algorithms and complexity, bioinformatics, information retrieval and database systems, symbolic computation, and quantum computing. The Cheriton School is a world leader in computer security and privacy, developing and researching tools used by millions of people every day to protect the security, privacy, and integrity of their online communications.