data science


Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition: Sebastian Raschka, Vahid Mirjalili: 9781787125933: Amazon.com: Books

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Sebastian Raschka, author of the bestselling book, Python Machine Learning, has many years of experience with coding in Python, and he has given several seminars on the practical applications of data science, machine learning, and deep learning, including a machine learning tutorial at SciPy - the leading conference for scientific computing in Python. While Sebastian's academic research projects are mainly centered around problem-solving in computational biology, he loves to write and talk about data science, machine learning, and Python in general, and he is motivated to help people develop data-driven solutions without necessarily requiring a machine learning background. His work and contributions have recently been recognized by the departmental outstanding graduate student award 2016-2017, as well as the ACM Computing Reviews' Best of 2016 award. In his free time, Sebastian loves to contribute to open source projects, and the methods that he has implemented are now successfully used in machine learning competitions, such as Kaggle. Vahid Mirjalili obtained his PhD in mechanical engineering working on novel methods for large-scale, computational simulations of molecular structures.


An indispensable Python : Data sourcing to Data science.

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Data analysis echo system has grown all the way from SQL's to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run all these operations { Don't forget the cost factor here:) }. Things become challenging when we mature from aggregated/summarized analysis to Data mining, mathematical modeling, statistical modeling and predictive modeling.


Feedback from ODSC London 2017

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ODSC gather a large community of Data Scientists around the world, with 3 organizations in Europe, West and East. Apart from the conference they hold every year, they also provide a newsletter, a job board and organize meetups to animate the community. At the ODSC London 2017 there were 10 training sessions, 28 workshops and 75 talks for 1500 attendees. The various topics covered were: Deep Learning, Predictive Analytics, Machine Learning, NLP, Cognitive Computing, AI, and Data Wrangling. Many tools were presented, from Big Data tools such as Apache Spark (SQL, Mllib, Streaming), Hadoop, Apache Storm and Apache Flink, to Deep Learning tools such as Tensorflow, Caffee, Torch, and some well known visualization tools like Neo4J, D3.js, R-Shiny.


Squeeze-and-Excitation Networks – Towards Data Science – Medium

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Squeeze-and-Excitation Networks (SENets) introduce a building block for CNNs that improves channel interdependencies at almost no computational cost. They were used at this years ImageNet competition and helped to improve the result from last year by 25%. Besides this huge performance boost, they can be easily added to existing architectures. As simple as it may sound, this is it. So, let's take a closer look at why this works so well and how we can potentially improve any model with five simple lines of code.


What Data Science Can Tell Us About Our World

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A daylong conference will cover a wide-range of topics related to computational data analysis, from how languages spread to ways of improving the value of crowdsourcing. The Data Science Workshop on Computational Social Science takes place Oct. 20. It's the first of what Dragomir Radev, the A. Bartlett Giamatti Professor of Computer Science, expects will be a regular event. "We decided we should try to cover different areas of data science," said Radev, one of the event's organizers. "We're starting with computational social science first and then switch to other areas in which data science and computer science have made an impact, for example, digital humanities, medicine, finance, etc." Radev said the event is something that likely would not have happened 10 or even five years ago.


BBC will use machine learning to cater to what audiences want to watch

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Today, BBC's R&D team announced a five-year initiative to use machine learning to work out what audiences want to watch. To accomplish this, the team is partnering up with data scientists and experts from UK universities as well as media and tech companies based in Europe and internationally. The Data Science Research partnership intends to create "a more personal BBC" that can entertain in new ways. Researchers will analyze user data and apply algorithms to get marketing and media insights about audiences' preferences. The details are vague for now, but the team says it plans to use machine learning on its own digital and traditional broadcasting content to gain new insights.


AI, ML & Deep Learning – Differences Explained by Experts

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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) – these are the three trending buzzwords that have created a great hype over the Internet and other media platforms for some time now. Irrespective of whether people hold a sound knowledge of the data science or not, everyone is actively making their own statements explaining the differences between these technologies, which thereby creating a mysterious situation for the newbies and laymen to understand the true differences between them. To make the things easy, this article will initially explain "what AI, ML, and DL are?", and later discusses the key differences between them. The definition of AI as per Wikipedia is – "the intelligence demonstrated by the In simple words, Artificial Intelligence (AI) can be referred as the'skill for a machine to exhibit its intelligent behavior'. Machine Learning as found in the Wikipedia is "the sub-field of computer science that gives computers the skill to learn without being explicitly programmed".


List Of 50 Unique/Routine AI Technologies – Hacker Noon

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The invention of artificial things that learn and perform actions took place in the classic times. Alongside Calculus Ratiocinator by Llull, there were many fictional stories and dramas depicting artificial things and their immense potentials. You must watch it if you haven't. Church-Turing thesis -- which means machines can simulate any process of formal reasoning (from Wiki). Theory that backed up the brains of creators like Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky, and Arthur Samuel.


Academic, Research Positions in Big Data, Data Mining, Data Science

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Samuel Kaski) - One of the core questions in machine learning at the moment is how to interact with humans. We turn this question into a probabilistic modelling problem, and model both the user and the task to drive the interaction. The solutions need combinations of probabilistic modelling, reinforcement learning and approximate Bayesian computation. We are looking for a postdoc who already masters some of these and offer an opportunity to learn the rest and work with us on this exciting bleeding-edge problem. Antti Oulasvirta) - The position offers an exciting opportunity to learn about and work on applications of machine learning methods and computational models of cognition, perception, and behavior in interactive systems.