This past week I had the good fortune of attending two great talks1 on Deep Learning, given by Googlers Ilya Sutskever and Jeff Dean. One of the most cited papers in the field showed that any continuous function can be approximated, to arbitrary precision, by a neural network with a single hidden layer. Deep networks also require some fiddling to optimize (in an earlier post I listed simplicity as one reason for choosing a specific model over another). One area where unsupervised learning for neural networks has been successful is in representing words as vectors in a high-dimensional space.
Researchers are working to enable smartphones and other mobile devices to understand and immediately identify objects in a camera's field of view, overlaying lines of text that describe items in the environment.
The innovation could find applications in "augmented reality" technologies like Google Glass, facial recognition systems and robotic cars that drive themselves. The research group has developed software and hardware and shown how it could be used to enable a conventional smartphone processor to run deep-learning software.
Research findings were presented in a poster paper during the Neural Information Processing Systems conference in Nevada in December. The new deep-learning capability represents a potential artificial-intelligence upgrade for smartphones.
The Purdue researchers initially worked with deep-learning pioneer Yann LeCun, Silver Professor of Computer Science and Neural Science at the Courant Institute of Mathematical Sciences and at the Center for Neural Science of New York University. The research has been funded by the Office of Naval Research, National Science Foundation and Defense Advanced Research Projects Agency.
A few days back, the content feed reader, which I use, showed 2 out of top 10 articles on deep learning.
Deep learning is probably one of the hottest topics in Machine learning today, and it has shown significant improvement over some of its counterparts. A large number of pixels are fed to the network as input, after which the network learns and evolves to recognize higher level features like faces and cats. This looks like a huge black box to deal with (something like a scaled up version of random forests), which might do well in data science competitions, but fails to deliver any business understanding. I have to admit, I started my research from a place where these algorithms looked more like a buzz. If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page. To conclude i think deep learning is not hype and it will change the way people look at machine learning today.


Machine learning is a branch of artificial intelligence (AI) based on two things – mathematical algorithms and automation. Ya seeing as Ray Kurzwell just got hired in 2009 by google to do hierarchical hidden models, the same guy that made the ibm watson and Nuance who sold it to ibm. Am pleased to share with you that while learning more on the deep learning, i found that Deeplearning4j is the first commercial-grade deep-learning library written in Java.
About Us For those of you, who are wondering what is “Analytics Vidhya”, “Analytics” can be defined as the science of extracting insights from raw data. Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising.
Much of the excitement surrounding Deep Learning stems from impressive results in a variety of perception tasks, including speech recognition (Google voice search) and visual object recognition (G+ image search). In practice this usually means learning a complicated function for handling a specified task (classify, cluster, predict, etc.).
This led some to think that neural networks with single hidden layers would do well on most machine-learning tasks. If you have a complex problem (such as recognizing objects in an image), Deep Learning can help but you’ll need to have access to large training data sets (deep networks work best when you have a very specific error rate that you need to minimize). Here, a street scene is labeled by the prototype, running up to 120 times faster than a conventional cell-phone processor.
Internet companies are using deep-learning software, which allows users to search the Web for pictures and video that have been tagged with keywords.
The poster paper was prepared by Martini; Culurciello and graduate students Jonghoon Jin, Vinayak Gokhale, Aysegul Dundar, Bharadwaj Krishnamurthy and Alfredo Canziani.
Research findings have shown the approach is about 15 times more efficient than conventional graphic processors, and an additional 10-fold improvement is possible.
So, let me summarize my findings and views on the topic, basis what I have read in the last few days. It falls under a class of unsupervised learning algorithms and uses multi-layered neural networks to achieve these remarkable outcomes.
Interest in neural networks peaked in the 1980s and 90s and then died off because of the inherent problems with them and black box like approach. According to a recent study, algorithms were able to classify the images on the left in the picture below, but were not able to classify the images on the right – which may seem very similar to human eyes.
But given the attention from data giants, success in some of the Kaggle competitions and the reducing costs of computations, I am starting to believe that the hotness of the field is justified.


In the meanwhile, I’ll continue with my research and keep you posted on how are things panning out at my end. Also, deep learning is the early term, the actual name is something else which I’m not able to recall right now. The idea is to automate the building of analytic models that use algorithms to “learn” from data in an iterative fashion.
It is meant to be used in business environments, rather than as a research tool for extensive data exploration.
One approach to machine learning mimics how the brain works: starting with basic building blocks (neurons), it approximates complex functions by finding optimal arrangements of neurons (artificial neural networks). Since lots of FLOPS are required, access to distributed compute resources and systems experts are essential. Classification of cats through unsupervised learning of YouTube videos was achieved by deploying 16,000 computers in Google lab!
For example, the model might say classify a particular person as a likely defaulter, but would not provide, why it is doing so. However, if you have limited degrees of freedom, we might end up with an overfitted model – probably time to go back to traditional methods. I heard the word Deep Learning few times in recent time, but thought it could be deepest version of machine learning.
Deeplearning4j is most helpful in solving distinct problems, like identifying faces, voices, spam or e-commerce fraud. The best way to pick up these critical bits of knowledge is to apprentice with more experienced users. I wanted to make sure this is a global phenomena and not just me getting served specific content based on my searches or past history. I hope to provide a meaningful summary to starters and a few thought provoking questions to the experts.
During this period he has lead teams of various sizes and has worked on various tools like SAS, SPSS, Qlikview, R, Python, Adobe Insight (Omniture) and Matlab. Early results using Bayesian optimization have been promising: automatic methods for selecting configuration parameters reached or surpassed human expert optimization.



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