Overview


Amazon Web Services, Inc.

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AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Who Should Attend: Data Scientists practitioners, Machine Learning practitioners, Deep Learning practitioners, Data Science students, Managers and Executives interested in deploying deep learning environments, anyone in a related field willing to know more about deep learning.


Highlights of EMNLP 2017: Exciting Datasets, Return of the Clusters, and More! - AYLIEN

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Four members of our research team spent the past week at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) in Copenhagen, Denmark. The current generation of deep learning models is excellent at learning from data. The Subword and Character-level Models in NLP workshop discussed approaches in more detail, with invited talks on subword language models and character-level NMT. Learning better sentence representations is closely related to learning more general word representations.


Object Detection: An Overview in the Age of Deep Learning

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There's no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. Similar to classification, localization finds the location of a single object inside the image. Going one step further from object detection we would want to not only find objects inside an image, but find a pixel by pixel mask of each of the detected objects. Object detection is the problem of finding and classifying a variable number of objects on an image.


Life 3.0: Being Human in The Age of Artificial Intelligence, A Review - Diplomatic Courier

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The computer, with a display of swirling dots around an orb, certainly didn't register the pseudo oath of fealty, but for many the defeat was a public display of artificial intelligence's (AI's) real arrival. Countless movies, not the least of which was the Terminator series, discussed the promise and peril of AI. Endorsed by Elon Musk, no less, Life 3.0 is an outstanding book that balances the highly technical computer science lexicon with real world questions about AI and the consequences for humanity if we do achieve artificial superintelligence. In attempting to answer these questions, Tegmark is successful.


Python Training Python For Data Science Learn Python

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This path provides a comprehensive overview of steps you need to learn to use Python for data analysis. The free interactive Python tutorial by DataCamp is one of the best places to start your journey. Now that you have learnt most of machine learning techniques, it is time to give Deep Learning a shot. In case you need to use Big Data libraries, give Pydoop and PyMongo a try.


AI in the Military: Paradigm Shift in Warfare - Scott Amyx

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The defense industry is the latest sector to utilize AI. With AI at helm, a central command could launch a multi-pronged attack from land, air, and water simultaneously without any humans on the warfront. The gun autonomously takes it own decision to fire on a target. Another potential drawback is ease of taking decisions to launch an attack when no human combatants are involved.


A Brief Overview of Outlier Detection Techniques – Towards Data Science – Medium

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Outliers can also come in different flavours, depending on the environment: point outliers, contextual outliers, or collective outliers. Point outliers are single data points that lay far from the rest of the distribution. In the process of producing, collecting, processing and analyzing data, outliers can come from many sources and hide in many dimensions. In machine learning and in any quantitative discipline the quality of data is as important as the quality of a prediction or classification model.


Fraud Prevention, Robo-Advisory Services, and Credit Scoring Transformed Through Machine Learning

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Frost & Sullivan's research, Disruption in Global Financial Services, 2017--Machine Learning is Imperative, provides an overview of ML market dynamics, including technology trends, drivers, and challenges for adoption. "ML enables speed and precision, which are crucial inputs to financial services companies' abilities to meet challenges related to efficiency and costs." Disruption in Global Financial Services, 2017--Machine Learning is Imperative is part of Frost & Sullivan's Digital Identification Growth Partnership Service program. Frost & Sullivan, the Growth Partnership Company, works in collaboration with clients to leverage visionary innovation that addresses the global challenges and related growth opportunities that will make or break today's market participants.


Regulating AI – The Road Ahead

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Summary: With only slight tongue in cheek about the road ahead we report on the just passed House of Representative's new "Federal Automated Vehicle Policy" as well as similar policy just emerging in Germany. Just today (9/6/17) the US House of Representatives released its 116 page "Federal Automated Vehicles Policy". Equally as interesting is that just two weeks ago the German federal government published its guidelines for Highly Automated Vehicles (HAV being the new name of choice for these vehicles). On the 6 point automation scale in which 0 is no automation and 5 is where the automated system can perform all driving tasks, under all conditions, the new policy applies to level 3 or higher (though the broad standards also apply to the partial automation in levels 1 and 2).


Machine Learning: An Overview

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Articles are shared online daily, software vendors and service providers have begun to offer Machine Learning as-a-service, thereby making it easier to integrate ML into your existing software products (no PhD required!). Unsupervised machine learning is the machine learning task of inferring a function to describe hidden structure from "unlabeled" data (a classification or categorization is not included in the observations). One example of this may be the machine attempts to organize data to describe its structure, thereby making it easier for humans to organize, and consequently derive meaning. Machine learning is behind all of this, it uses YOUR historical big data and other data sets such as similar consumers' past behavior to drive consumer recommendations.