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Machine Learning Vs Deep Leaning – An Analysis

Machine Learning

Know about create a digital signature what is the difference between Machine Learning and Deep Learning?  – This is a question that we regularly face both in my personal life and at work.  As an undergraduate studying Mechatronics Engineering, I was initially overwhelmed by the amount of information available about both subjects.  This article will be an attempt to clear up some of the confusion surrounding these topics.

A machine learning system tries to solve tasks without being explicitly programmed to do so.  An example would be self-driving cars or spam filtering software. The system learns how to accomplish its task through experience rather than to follow a set of instructions written out beforehand (more on this later).   It may sound like machine learning systems are intelligent – but in reality, all they are accomplishing is optimizing a function.  Machine learning systems are beneficial but are inherently limited because their decision-making process has to be predefined.

Machine learning:

Machine Learning is not equivalent to Deep Leaning.  Deep Leaning comprises neural networks combined with algorithms that learn how to model complex functions.  Neural networks have been around for decades but recently have experienced resurgence due to the availability of big data and more computational power in small devices.

Deep Learning systems try to solve tasks by learning how to map input data (examples) to output data through experience instead of being explicitly programmed.   For example, Google’s voice search learns which words you are likely saying based on your past searches, and its speech recognition systems learn how to interpret different accents based on the input of native speakers.

Deep Learning

Deep Learning is similar to machine learning in that it tries to solve tasks without being explicitly programmed for them.  Deep Learning can be used to classify images by training a neural network with example pictures (this is how Google’s image search works).

However, there are many problems where using Machine Learning would be more appropriate compared to Deep Leaning – the biggest reason being the lack of Big Data available for most problems.  When trying to solve an extremely complex problem such as self-driving cars, you likely need both techniques working in conjunction.  Google uses self-driving cars combined with street view data to train their neural networks, then used to operate the self-driving car.

The original Machine Learning algorithms have been around for decades but recently have experienced a resurgence due to the availability of big data and more computational power in small devices.  The idea is that rather than having explicit instructions programmed into an algorithm, you give it examples (input data) and let it learn how to accomplish its task (for example, object recognition).

You can think of this technique as “20 questions” – the reason why the game works so well is that there’s no way you can anticipate every possible answer beforehand. This approach also applies well to machine learning: if all possible answers (output data) can be anticipated and pre-programmed, it’s not worth using machine learning.

Machine Learning Algorithms:

The original Machine Learning algorithms try to solve tasks by learning how to map input data (examples) to output data through experience instead of explicitly programming.  The machine learning algorithm tries different mappings (answers) until the best matching mapping is found.

This process is done automatically without human interaction or intervention, so it becomes computationally expensive when the problem size becomes large.  Machine learning problems typically need many examples to account for all possible answers – this is where Deep Leaning techniques come into play according to professionals at RemoteDBA.com.   

Deep Learning comprises neural networks combined with algorithms that learn how to model complex functions.  Deep Neural Networks (DNNs) can be used in  , and more recently have found great success by the availability of big data and more computational power in small devices.

Examples to note

For example, Google’s voice search learns which words you are likely saying based on your past searches, and its speech recognition systems learn how to interpret different accents based on the input of native speakers.

Deep Leaning is similar to machine learning. It tries to solve tasks without being explicitly programmed; however, DNNs try to solve tasks using DNNs combined with algorithms that learn how to model complex functions such as speech recognition or language generation.

The biggest reason being the lack of Big Data available for most problems.  When trying to solve an extremely complex problem such as self-driving cars, you likely need both techniques working in conjunction with each other: Google uses self-driving cars combined with street view data to train their neural networks, which are then used to operate the self-driving car.

This approach also applies well to machine learning: if all possible answers (output data) can be anticipated and pre-programmed, it’s not worth using machine learning.   A history of artificial neural networks dates back to the 1940s, when McCulloch and Pitts proposed that neurons are simple computational devices that take in an input, change it through some algorithmic process, and produce an output. However, they did not consider how difficult this algorithm would be to implement for real neurons.

As computers were developed in the 1960s, this started to become more plausible. The first algorithms implemented on early computers simulated a single neuron very poorly (in fact, nothing like a real neuron at all). Still, by the 1980s, these had grown into perceptions able to recognize simple patterns. However, it was not until 1986, with GPU technology available, that people could successfully train large, fully connected deep feed forward neural nets.

Training deep nets with back propagation (the most popular method) requires the forward and backward passes through the network to be computed simultaneously, which was not possible until recently. Deep learning research went through a lull between 1990 and 2006 but has seen something of an explosion since then, driven by improved algorithms (e.g. introduction of AdaGrad), better hardware (e.g. GPUs ) and large scale digitized information becoming available on the Internet. As well as neural networks, support vector machines have been very influential during this time frame.

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