programming


Expert Panel Debunks AI Hype EE Times

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"Turing predicted [that] AI will exceed human intelligence, and that's the end of the race -- if we're lucky, we can switch them off," said Stuart Russell, a professor of computer science at Berkeley and AI researcher, now writing a new version of a textbook on the field. He noted that a neural network is just part of Google's AlphaGo system that beat the world's best players. "AlphaGo … is a classical system … and deep learning [makes up] two parts of it … but they found it better to use an expressive program to learn the rules [of the game]. An end-to-end deep learning system would need … [data from] millions of past Go games that it could map to next moves.


Top Data Science Resources on the Internet right now

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The Andrew Ng course felt like black magic. You will learn about Python Libraries like Numpy,Pandas for data science, along with a thorough intuitive grinding for various Machine learning Algorithms.


Vincent Granville

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The data was stored in an hierarchical database (digital images based on aerial pictures, the third dimension being elevation, and ground being segmented in different categories - water, crop, urban, forest etc.) The data was stored in an hierarchical database (digital images based on aerial pictures, the third dimension being elevation, and ground being segmented in different categories - water, crop, urban, forest etc.) Markov Chains Monte Carlo modeling (Bayesian hierarchical models appied to complex cluster structures) Spatio-tempral models Environmental statistics: storm modeling, extreme value theory, and assessing leaks at the Hanford nuclear reservation (Washington State), using spatio-temporal models applied to chromium levels measured in 12 wells. Environmental statistics: storm modeling, extreme value theory, and assessing leaks at the Hanford nuclear reservation (Washington State), using spatio-temporal models applied to chromium levels measured in 12 wells.


How to hire a great Data Scientist - Saikat Sarkar, Data Science Exper

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He is presently acting as the Subject Matter Expert of Python in Aegis School of Data Science. He also talks about the importance of the Data Science domain today and in the years to come. Typically, a company should look at candidates with fairly strong programming skills and statistical understanding of ML. Modern day ML concepts are not known by the senior people of the organisation, they have worked on old school statistics, so that's the field they try to drill the candidates in.


The Guide to Learning Python for Data Science

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We will discuss steps you should take for learning Python accompanied with some essential resources, such as the free Python for Data Analysis courses and tutorials from DataCamp as well as reading and learning materials. The most convenient way to go about this is to download the free Anaconda package from Continuum Analytics, as it contains the core Python language, as well as all of the essential libraries including NumPy, Pandas, SciPy, Matplotlib, and IPython. The analytics begins with statistical modeling, machine learning algorithms, data mining techniques, inferences and so on. Of course, as Python is a general purpose programming language, you are also free to program your own methods when you become an advanced user, though make sure you are not replicating what already exists.


Resources for getting started with Python and machine learning

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In this article, I'll share a few of the best resources that helped me advance from building my first program to building my first neural network. Deep Learning is a technique using neural networks for machine learning. Providing a Good Education in Deep Learning emphasizes how inclusiveness should be a key responsibility in education pertaining to transformative technologies such as AI. Ideally, it would be great to have a programming resource that taught Python and machine learning concurrently, but I haven't found one yet.


Artificial Intelligence: Made Easy w/ Ruby Programming; Learn to Create your * Problem Solving * Algorithms! TODAY! w/ Machine Learning & Data ... engineering, r programming, iOS development): Code Well Academy: 9781530826865: Amazon.com: Books

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Well... it's not a bad book but I don't think it covers "AI" in more than a very generic sense. The author introduces a few simple techniques for resolving schedules via brute force iterative approach (not inappropriate for many use cases), simple case logic, and a few tree/acyclic graph solvers. Ruby code is given for all approaches and it's easy to follow. If you're not in that category, I'd skip it and pick up a good general algorithm text covering data structures and searching.


Quant Guide 2017: Princeton University - Risk.net

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"They have the five core courses, but then they can take machine learning, artificial intelligence, computer science, public policy, PhD workshops in econometrics." The programme draws on the expertise of a number of departments, including the departments of operations research and financial engineering, economics, and computer science. Courses that cover artificial intelligence and machine learning have traditionally been offered by Princeton's engineering and computer science-focused programmes, but now they're also available as part of the 11 elective courses offered by the master in finance programme. The core courses are focused on economics, maths, finance, probability and statistics.


a-journey-through-time-the-long-prehistory-of-artificial-intelligence

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According to Aristotle, while living things moved themselves at will, inanimate things moved according to their natures: heavy things, made of earth or water, descended, while light things, made of air or fire, ascended. Twenty years later, the French King Henri IV hired the Italian engineer Tomaso Francini to build him some waterworks for the royal palace at Saint Germain en Laye. In 1650, the German polymath Athanasius Kircher offered an early design of a hydraulic organ with automata, governed by a pinned cylinder and including a dancing skeleton. The designers of the automatic loom used automata and automatic musical instruments as their model; then Charles Babbage -- the English mathematician who designed the first mechanical computers during the 1830s, the Analytical and Difference Engines -- in turn used the automatic loom as his model.


The Best Data Science Courses on the Internet, Ranked by Your Reviews

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We compiled average ratings and number of reviews from Class Central and other review sites to calculate a weighted average rating for each course. Big Data University's Data Science Fundamentals covers the full data science process and introduces Python, R, and several other open-source tools. An effective practical introduction, Kirill Eremenko's Tableau 10 series focuses mostly on tool coverage (Tableau) rather than data visualization theory. Kirill Eremenko and Hadelin de Ponteves' Machine Learning A-Z is an impressively detailed offering that provides instruction in both Python and R, which is rare and can't be said for any of the other top courses.