Here you will learn about difference between deep learning and machine learning.
We already are aware of the term and in brief that Deep Learning is the subset of a wider domain called Machine Learning. If talking combined of Machine Learning and Deep Learning we can think of how Netflix is able to predict and recommend shows to watch based on your taste and how Facebook is able to recognize the face in the pictures you upload.
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Also as Machine Learning is the superset of Deep Learning, Artificial Intelligence is the superset of Machine Learning. So, instead of using these terms interchangeably we should be able to distinguish between them.
Deep Learning is being used by Google in its image and voice recognition algorithms, by Amazon to predict and recommend what a customer wants next and by MIT researchers to predict the future.
Before moving further let us quickly get a brief intro of what Deep Learning actually do so as to maintain its existence. It is quite clear that it will be focusing on the principles of ML and AI being subset of them. So come let’s see what’s new there for us to learn.
How does Deep Learning work?
Primarily the basic concept behind Deep learning is to feed the computer with decent amount of data/information which can be later used for the decision making process about some other set of data. Now you might be wondering, how the heck are we going to feed the computer? Is that similar to the process we adopted in case of ML? Yes, you got this right. The exact same method as ML is also entertained in deep learning i.e. via Neural Networks.
These networks are also termed as logical constructions which classify every bit of data that passes from them on the basis of answers received to every binary (TRUE/FALSE) questions being asked to the bits passing via network. Since Deep Learning is associated with the perspective of developing these networks, therefore are also known as Deep Neural Networks. Such networks are witnessed to process comparatively large datasets like Google’s image library, or Facebook’s feeds repository. Now we can very easily get an idea of what efforts are needed by computers to handle such a large datasets with extremely sophisticated networks. On the other hand how all these tasks are accomplished by humans with intense ease is remarkable.
Working of deep neural networks are better tested with images as inputs because of the fact that images consist of several different elements and it is pretty interesting to observe that how computer with its calculation-oriented, one track mind can learn to identify and distinguish the images like we humans do.
Note: Deep Learning can also be applied on several other types of data such as signals, speech, audio, video, written texts, etc. to produce conclusions.
Let us make this explanation bit easy to understand with the help of an example.
Problem Statement: To take input of all the cars passing along a public road and classify them on the basis of make and model.
Solution: First step towards the solution would be to provide the system access to the large database containing the information about the cars (like shape, size, engine sound, etc.). This can be accomplished manually or in the most advanced manner where the system can be programmed to search the internet for the relevant information and interpret the information found there.
Next step would be the intake of the data that needs to be processed. In this case the images and sound captured by cameras, microphones and other sensors are input to the system. The data from the sensors are compared with the data already being present within the system or the data what system has learned. And thus the system is able to classify the cars on the basis of their make and model with certain probability of accuracy.
Up till now this all was pretty straightforward. Now, the interesting part comes in when we talk about “Deep Learning” in this, as the time passes, the system gains more and more experience and become more able to classify the cars after being trained on new data with improved probability every time, like humans do. The system also learns from the mistakes that it make during the classification process just like humans do and with passing time the accuracy is observed to be improved significantly.
Some of the noteworthy work and examples of Deep Learning are self-driving cars, predicting the outcome of legal proceedings, precision medicine, game playing and many more.
Note: In order to dive deeper in context of deep learning you may refer to Bernard Marr’s new book Data Strategy.
Difference between Deep Learning and Machine Learning
As already told in the beginning of this post, Deep Learning is the subset of Machine Learning. A machine learning model needs to be told explicitly by feeding more and more data that how it should be making accurate prediction, on the contrary the deep learning model is capable of self-learning through its own method of computing (so-called its own brain).
A deep learning model is designed in a way so as to interpret the data with some logic structure to copy the human’s ability of drawing conclusions. To accomplish this with simplicity and ease deep learning models uses a layered structure of algorithms known as Artificial Neural Network (ANN), whose structure is known to be very similar to the biological neural network present in human beings. Due to these facts the models made following the principles of deep learning are observed to be far more capable in the decision making process when compared to a typical machine learning model.
Despite of all complex networks resembling a human neuron, it is not always ensured that models pertaining to deep learning will not draw an incorrect conclusion. Also observing the degree of certainty the deep learning models are considered as the potential support to AI. One of the noteworthy accomplishment in the field of deep learning is Google’s AlphaGo. Google created a deep learning model that become expert in a board game “Go” by playing against professional Go players and learning step by step what moves to make. The model was too featured in the news when it defeated multiple times winner.
In the last we think we should have a quick recap of what we have learnt so far in this post. To get a better grasp, let us do this in a tabular way.
| Machine Learning | Deep Learning |
| Machine Learning is a subset of AI | Deep Learning is a subset of ML |
| Such models uses data to learn from them and then make decisions accordingly. | These models are capable of making any decision on their own. |
| Established algorithms are the basis to ML. | Artificial Neural Networks (ANN) forms the foundation of deep learning. |
| AI is the superset. | AI again is the superset. |
| ML models have set some great examples like weather forecasting. | Deep learning has got ML covered by providing even better capabilities to the existing ML models. |
| Examples: face recognition, spam filtering, weather forecasting, etc. | Examples: Google’s AlphaGo, Tesla’s self-driving car, etc. |
End Notes
We are pretty sure that our readers must be fascinated with the facts and figures we’ve discussed in this post regarding deep learning. Moreover as we have seen that a huge amount of data is required to fuel up the deep learning vehicle, this domain is believed to prosper in this era of big data and in times to come. It is believed that in the coming decades, deep learning would be having examples that humans cannot even imagine of.
In an interview with Wired Magazine, Baidu’s chief scientist Andrew Ng was reported to say : “I think AI is akin to building a rocket ship. You need a huge engine and a lot of fuel, if you have large engine and a tiny amount of fuel, you can’t even lift off. To build a rocket you need a huge engine and a lot of fuel.” (Source: Wired)
In the last we would like to thank our readers, We hope you guys enjoyed this post. Any suggestions or queries are always welcomed from readers end. If there exist any doubt or ambiguity regarding Deep Learning vs Machine Learning, please do let us know in the comment section below. Also let us know what other topics do you want us to write about, we would be more than happy.
