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Applications and Types of Machine Learning


What are the applications of machine learning? According to Wikipedia: Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine Learning has opened a new vista of marketing and business process optimization in the retail sector.

Deep Learning Applications for Enterprise with Skymind's Chris Nicholson -


Episode Summary: In one of our most recent consensus, we took a close look at future trends in artificial intelligence consumer applications, but it's also interesting to see what's happening now in businesses. Chris Nicholson is the CEO of, which offers deep learning applications that integrate with Hadoop and Spark. In this episode, Nicholson sheds light on current trends that he sees across industries and best practices for implementing AI solutions to gain consistent return on investment. Brief Recognition: Chris Nicholson leads Skymind, the commercial support arm of the open-source framework Deeplearning4j. Skymind helps companies in telecommunications, finance, retail and tech build enterprise deep learning applications, notably fraud detection, using data such as text, time series, sound and images.

Are You Joining The Machine Learning Revolution?


Have you noticed that the better you know someone, the easier it is to communicate with them? When we are particularly close, this can border on the telepathic as we start to anticipate what the other person is going to say and finish their sentences. Unconsciously, our brains are collecting, processing, storing, and recalling a huge range of verbal and nonverbal signals, then translating this learning and familiarity into actions. Of course, we're a long way from understanding – let alone replicating – the infinite complexities of the human brain. But in the simplest of terms, this is how machines can learn to interact with people.

One sketch for all: Theory and Application of Conditional Random Sampling

Neural Information Processing Systems

It was previously presented using a heuristic argument. This study extends CRS to handle dynamic or streaming data, which much better reflect the real-world situation than assuming static data. Compared with other known sketching algorithms for dimension reductions such as stable random projections, CRS exhibits a significant advantage in that it is one-sketch-for-all.'' Although a fully rigorous analysis of CRS is difficult, we prove that, with a simple modification, CRS is rigorous at least for an important application of computing Hamming norms. A generic estimator and an approximate variance formula are provided and tested on various applications, for computing Hamming norms, Hamming distances, and $\chi 2$ distances.

AI on the Edge


The next evolution in cloud computing is a smarter application not in the cloud. As the cloud has continued to evolve, the applications that utilize it have had more and more capabilities of the cloud. This presentation will show how to push logic and machine learning from the cloud to an edge application. Afterward, creating edge applications which utilize the intelligence of the cloud should become effortless.