Machine Learning and Deep Learning are concepts that are often overlapping. There can be a slight confusion between the terms, and thus, let us look at Machine learning vs Deep learning, and understand the similarities and differences between the same.
Machine learning uses a set of algorithms to analyse and interpret data, learn from it, and based on the learnings, make best possible decisions. On the other hand, Deep learning structures the algorithms into multiple layers in order to create an “artificial neural network”. This neural network can learn from the data and make intelligent decisions on its own.
Deep Learning vs. Machine Learning
|Machine Learning||Deep Learning|
|Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned||Deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own|
|Can train on lesser training data||Requires large data sets for training|
|Takes less time to train||Takes longer time to train|
|Trains on CPU||Trains on GPU for proper training|
|The output is in numerical form for classification and scoring applications||The output can be in any form including free form elements such as free text and sound|
|Limited tuning capability for hyperparameter tuning||Can be tuned in various ways|
Now that we are aware of some of the differences between deep learning and machine learning, let us try to understand them better.
What is deep learning? How is it related to machine learning? Is it better than conventional machine learning? When, where, and why is deep learning used? A lot of questions at once, isn’t it? Deep learning is a part of the machine learning family which is based on the concept of evolutionary algorithms. It basically mimics biological processes like evolution.
Let’s look at another question. Which came first? The chicken or the egg?
Centuries have passed and we haven’t been able to answer this question. But soon, maybe a machine will! Can it? Will it? Let’s figure out!
Machine learning is a subset of Artificial Intelligence that uses statistical strategies to make a machine learn without being programmed explicitly using the existing set of data. It evolved from the study of pattern recognition in Artificial Intelligence. For a detailed understanding of machine learning, you can watch this video-
Conventional machine learning methods tend to succumb to environmental changes whereas deep learning adapts to these changes by constant feedback and improve the model. Deep learning is facilitated by neural networks which mimic the neurons in the human brain and embeds multiple-layer architecture (few visible and few hidden). It is an advanced form of machine learning which collects data, learns from it, and optimises the model. Often some problems are so complex, that it is practically impossible for the human brain to comprehend it, and hence programming it is a far fetched thought. Primitive forms of Siri and Google assistant are an appropriate example of programmed machine learning as they are found effective in their programmed spectrum. Whereas, Google’s deep mind is a great example of deep learning. Essentially, deep learning means a machine which learns by itself by multiple trial and error methods. Often a few hundred million times!
A simple understanding of basic deep learning concepts can be grasped from this video on neural networks:
Now that was pretty impressive, right?
Let us think of writing a program which differentiates between an apple and an orange. Although it may sound like a simple task to accomplish, it is indeed a complex one as we cannot program a machine to know the difference merely by observing it. We as humans can, machines can’t! So if we were to program, we would mention a few specifications of the apple and the orange but it would work for simple and clear images like these.
But what if we place a banana?
The machine would probably be befuddled! This is where deep learning comes into the picture. A conventional machine learning method helps a machine to efficiently perform only a predetermined set of instructions and tends to become unworthy in case new variables are introduced in the system. It can be understood better with this video:
Deep learning helps a machine to constantly cope with the surroundings and make adaptable changes. This ensures versatility of operation. To elaborate, deep learning enables a machine to efficiently analyse problems through its hidden layer architecture which are otherwise far more complex to be programmed manually. So, deep learning gets an upper hand when handling colossal volumes of unstructured data as it does not require any labels to handle the data.
So Let’s Summarise
Deep learning is an advanced form of machine learning which comes in handy when the data to be dealt with is unstructured and colossal. Thus, deep learning can cater to a larger cap of problems with greater ease and efficiency. Technological breakthroughs like Google’s Deepmind is the epitome of the heights that current AI can reach, facilitated by deep learning and neurological networks.
So maybe we can’t predict which came first, the chicken or the egg but will AI be able to? Stick around to find out!
Machine learning and deep learning can be daunting and difficult to learn by yourself. A gamut of online free courses have come forward to make things simpler but if you want to take up a rigorous well-respected course that employers will respect then the Post Graduate Program in Machine Learning by Great Learning which offers 130 hours of content and personalised mentorship in an extremely easy to grasp manner is an excellent choice.
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