{"id":122551,"date":"2015-02-26T17:57:43","date_gmt":"2015-02-26T12:27:43","guid":{"rendered":"http:\/\/analyticstraining.com\/?p=6040"},"modified":"2022-07-14T16:44:36","modified_gmt":"2022-07-14T11:14:36","slug":"deep-learning-next-big-step-towards-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.jigsawacademy.com\/deep-learning-next-big-step-towards-artificial-intelligence\/","title":{"rendered":"Deep Learning: The next big step towards Artificial Intelligence"},"content":{"rendered":"

“Deep learning” a relatively new field of artificial intelligence research promises general, powerful, and fast machine learning, moving us one step closer to AI. It tries to mimic the activity of the brain by using so-called neural networks.<\/span><\/span><\/span><\/p>\n

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But how did it turn from an uncertain academic topic into one of tech’s most exciting fields in under a decade? What is its business impact? How different is it from Machine learning? Which top organisations are working on it? <\/span><\/span><\/p>\n

What is Machine Learning?<\/b><\/span><\/span><\/p>\n

In a\u00a0nutshell, ML is field of Computer science that uses statistical (or mathematical) techniques to construct a model (or system) from observed data rather than have user enter specific set of instructions that define the model for that data.<\/span><\/span><\/p>\n

Some common examples where ML is used in businesses is trying to find out if your customer is loyal or not, whether a customer will make a purchase or not, predict the sentiment of a document, etc. A more complex example could be predicting fluctuating stock market prices.<\/span><\/span><\/p>\n


\nMost often, these algorithms work on precise set of features extracted from your raw data. Features could range from something as basic as age of a customer, number purchases a customer made, etc. to something as complex as pixel values for images, bag of words representation for text data, etc. So better the \u201cFeatures\u201d that represent the data, the more accurate your algorithm will be. The feature extractors are used to extract correct data features for a given sample, and pass this information to a classifier\/ predictor. But machine learning algorithms are considered very \u201cshallow\u201d. Now why is that? This is for the simple reason that for ML algorithms, we need to provide lots and lots of training examples and the most accurate feature set that will train your algorithm well, thus manually correcting its mistakes. So, to avoid this human intervention, we have a new generation of ML algorithms called \u201cDeep Learning\u201d, being developed.<\/span><\/span><\/p>\n

The need for Deep Learning <\/b><\/span><\/span><\/p>\n

\u201cDeep\u201d learning algorithms attempt to model high-level abstractions (features) in data by using model architectures composed of multiple non-linear transformations.<\/span><\/span><\/p>\n

Unlike machine learning, deep learning is mostly unsupervised, i.e. for example, creating large-scale neural nets (neural networks technique) that allow the computer to learn and “think” by itself without the need for direct human intervention.<\/span><\/span><\/p>\n

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