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7 Types of Regression Techniques you should know. Introduction Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling.

7 Types of Regression Techniques you should know

Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important amongst all forms of regression analysis. The truth is that there are innumerable forms of regressions, which can be performed. Each form has its own importance and a specific condition where they are best suited to apply. Table of Contents. A Machine Learning Introductory Tutorial with Examples. Machine Learning (ML) is coming into its own, with a growing recognition that ML can play a key role in a wide range of critical applications, such as data mining, natural language processing, image recognition, and expert systems.

A Machine Learning Introductory Tutorial with Examples

ML provides potential solutions in all these domains and more, and is set to be a pillar of our future civilization. The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This tutorial introduces the basics of Machine Learning theory, laying down the common themes and concepts, making it easy to follow the logic and get comfortable with the topic. Meanderful: The age of perception: a $20 trillion dollar per year opportunity. During the last two to three years, the world has changed.

Meanderful: The age of perception: a $20 trillion dollar per year opportunity

Perception has been solved. Computers were bad at it. Now they are good at it. The main culprit is the new generation of artificial neural nets branded as deep learning. Great leaps have been made in image, video, speech and text semantic analysis. What's the likely economic impact? On 30 April 2015, PWC released a good whitepaper on STEM with support from the Centre of Policy Studies (CoPS) at Victoria University. What does at risk mean? PWC quote an impact of a benefit of some $57 billion being added to GDP for an increase of 1 percent in STEM roles for Australia. How a Toronto professor’s research revolutionized artificial intelligence. MOUNTAIN VIEW, CALIF.

How a Toronto professor’s research revolutionized artificial intelligence

Music Recommendations at Scale with Spark. Creating a Multimodal Recommender with Mahout and a Search Engine. Deeplearning4j - Open-source, distributed deep learning for the JVM. Machine Learning Engineer Nanodegree. About -Skymind. Skymind is a business intelligence and enterprise software firm based in San Francisco, California.

About -Skymind

We analyze text media, image, video, time series and sound to locate and quantify patterns that impact your business. Deeplearning4j - Open-source, distributed deep learning for the JVM. Classification datasets results. Visual Information Theory. Posted on October 14, 2015 I love the feeling of having a new way to think about the world.

Visual Information Theory

I especially love when there’s some vague idea that gets formalized into a concrete concept. Neural networks and deep learning. When a golf player is first learning to play golf, they usually spend most of their time developing a basic swing.

Neural networks and deep learning

Only gradually do they develop other shots, learning to chip, draw and fade the ball, building on and modifying their basic swing. Home - colah's blog. The Mystery of Go, the Ancient Game That Computers Still Can’t Win. TOKYO, JAPAN — Rémi Coulom is sitting in a rolling desk chair, hunched over a battered Macbook laptop, hoping it will do something no machine has ever done.

The Mystery of Go, the Ancient Game That Computers Still Can’t Win

That may take another ten years or so, but the long push starts here, at Japan’s University of Electro-Communications. The venue is far from glamorous — a dingy conference room with faux-wood paneling and garish fluorescent lights — but there’s still a buzz about the place. Spectators are gathered in front of an old projector screen in the corner, and a ragged camera crew is preparing to broadcast the tournament via online TV, complete with live analysis from two professional commentators. The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. But faced with massive data, this approach to science — hypothesize, model, test — is becoming obsolete.

The End of Theory: The Data Deluge Makes the Scientific Method Obsolete

Consider physics: Newtonian models were crude approximations of the truth (wrong at the atomic level, but still useful). A hundred years ago, statistically based quantum mechanics offered a better picture — but quantum mechanics is yet another model, and as such it, too, is flawed, no doubt a caricature of a more complex underlying reality. The reason physics has drifted into theoretical speculation about n-dimensional grand unified models over the past few decades (the "beautiful story" phase of a discipline starved of data) is that we don't know how to run the experiments that would falsify the hypotheses — the energies are too high, the accelerators too expensive, and so on. DH - Publications.

[quant-ph/9908043] Ultimate physical limits to computation. 3# Xerox PARC Forum: AI and Robotics at an Inflection Point. By omohundro On September 18, 2014 Steve Omohundro gave the Xerox PARC Forum on “AI and Robotics at an Inflection Point”.

Xerox PARC Forum: AI and Robotics at an Inflection Point

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