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Bay Area Vision Meeting: Unsupervised Feature Learning and Deep Learning

Despite machine learning’s numerous successes, applying machine learning to a new problem usually means spending a long time hand-designing the input representation for that specific problem. This is true for applications in vision, audio, text/NLP, and other problems. To address this, researchers have recently developed “unsupervised feature learning” and “deep learning” algorithms that can automatically learn feature representations from unlabeled data, thus bypassing much of this time-consuming engineering. Building on such ideas as sparse coding and deep belief networks, these algorithms can exploit large amounts of unlabeled data (which is cheap and easy to obtain) to learn a good feature…

O’Reilly Webcast: Deep Learning – The Biggest Data Science Breakthrough of the Decade

Machine learning and AI have appeared on the front page of the New York Times three times in recent memory: 1) When a computer beat the world’s #1 chess player 2) When Watson beat the world’s best Jeopardy players 3) When deep learning algorithms won a chemo-informatics Kaggle competition.   We all know about the first two… but what’s that deep learning thing about? This happened in November of last year, and it represents a critical breakthrough in data science that every executive will need to know about and react to in the coming years. The NY Times said that…

Machine learning – Deep learning I

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.  

Trends in Deep Learning

This talk gives a brief history of deep learning architectures, moving into modern trends and research in the field. Key points of discussion are neural activation functions, weight optimization strategies, techniques for hyper-parameter selection, and example architectures for different problem sets. We finish with a few notable examples of “web scale” deep learning at work.   This talk will focus on (briefly) sklearn, Theano, pylearn2, theanets, and hyperopt.  

Machine Learning Discussion Group – Deep Learning w/ Stanford AI Lab (1 of 3)

Adam Coates will give an overview of some recent research projects from the Stanford Artificial Intelligence Lab and will do a presentation with open discussion on Deep Learning, an exciting recent addition to the machine learning algorithm family. The format will be interactive as Adam will answer questions from the group. So this will be a great opportunity to learn from one of the authorities on this exciting topic.  

Principles of Hierarchical Temporal Memory (HTM): Foundations of Machine Intelligence

Hierarchical temporal memory (HTM) is a biologically constrained theory (or model) of intelligence, originally described in the 2004 book On Intelligence by Jeff Hawkins with Sandra Blakeslee. HTM is based on neuroscience and the physiology and interaction of pyramidal neurons in the neocortex of the mammalian (in particular, human) brain.  

Deep Learning of Representations

Yoshua Bengio will give an introduction to the area of Deep Learning, to which he has been one of the leading contributors. It is aimed at learning representations of data, at multiple levels of abstraction. Current machine learning algorithms are highly dependent on feature engineering (manual design of the representation fed as input to a learner), and it would be of high practical value to design algorithms that can do good feature learning. The ideal features are disentangling the unknown underlying factors that generated the data. It has been shown both through theoretical arguments and empirical studies that deep architectures…

How To Create A Mind: Ray Kurzweil at TEDx Silicon Alley

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.  

Electronics Tutorial #1 – Electricity – Voltage, Current, Power, AC and DC

This tutorial covers the following: * Some history about electricity / central power stations / electrification. * Science of electron flow in a conductor / wire * I use fluid dynamics in a pipe to explain voltage (pressure), current in Amps (flow) and consumption in Amp hours (rate of flow). * We look at AC (alternating current) and DC (direct current) on the UNI-T UT81B scopemeter * We look at how voltage / pressure is required to charge a 12 volt battery * What is electricity? / How does electricity work?  

Arduino Uno: control circuits and homebuilt servos

This tutorial Contains the following: * introduction * boot process * basic input/output * simple computing * optical sensors scanning disc * controlling an H bridge * analog inputs (ADC) * controlling servos * improving the homebuilt wiper servo * PID control loop * soft start and emergency stop * controlling a combustion engine * linear servo with digital sensor  

Profibus vs Modbus│ Difference Between Modbus and Profibus Communication│

There are some different of Modbus and Profibus which are sometimes called “Fieldbus”. It is a common name used to tell any network industry that links the field devices. As a generic explanation it was fashionable at one point, but looks to have fallen out of use in recent times.   It is also occasionally applied as the simple form of a particular protocol called Foundation Fieldbus. It is only one more protocol such as Profibus or Modbus, but it has attributes which are planned specifically for industrial processes.   It was also used as the short form for a…

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