What is the Difference Between Machine Learning and Deep Learning

By Great Learning Team Updated on May 24, 2022 44068

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.

Background

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.

To get in-depth knowledge on Deep learning, do check out our Free Course on Deep Learning and Machine Learning at Great Learning Academy. During the course for Deep Learning, you will also understand the basics of Linear Algebra such as Tensors, Scalars, Vectors, Matrix, Determinants, and Eigenvalues and Eigenvectors. Also, basics of Calculus such as derivatives and Gradient Descent. During the course for Machine Learning, you will understand the core concepts of Machine Learning, Supervised, Unsupervised, and Reinforcement Learning.

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!

So what exactly is machine learning?

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.

Deep Learning

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!

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.

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!

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