It is no news that we’re witnessing rapid advancement in the field of data science and technology.
So much so, that discussing buzzwords such as artificial intelligence, algorithms, computer vision, deep learning, and such is becoming increasingly commonplace in daily life.
Due to this, people get confused between the terms and what they stand for. This is because, despite these terms all being related, they aren’t interchangeable.
Here’s a handy guide to understanding the two terms deep learning and machine learning so you can learn what makes them different from each other.
The simplest way to understand what deep learning is to know that deep learning is machine learning.
But is that all? No.
Deep learning and machine learning both fall under the concepts of machine intelligence, which falls under the broader term artificial intelligence. Therefore, to understand what these two terms mean, we need to look at what artificial intelligence (AI) stands for.
What is AI?
A subfield of data science, AI or Artificial Intelligence is the term used to explain how machines mimic human intelligence and some cognitive functions to solve complex problems. Programmers train computers to think, learn and make decisions as humans would by leveraging their processing capability.
AI is everywhere around us- in the game of chess or checkers you play with the computer, in the auto-fill option on your phone, in the smart suggest a feature that brings up the best hotels close to the last place you googled, in the chatbot you interact with on a health and well-being application, and in your recommendations on Netflix and Spotify.
None of these activities could have been possible without the advancement in AI technology.
What is machine learning?
Machine learning (ML) is a subfield or subset of AI which, just as the name suggests, indicates that the machine “learns” from the data input. Because the machine is constantly learning, the functioning of the machine gets progressively better over time.
The machine system first needs data input from a programmer. This data is mostly structured.
After receiving data, the machine recognizes patterns in the data input and makes predictions when it obtains new data without explicit programming. This allows the system to learn how to improve its performance and find more accurate results with experience.
The programmer need not program - in other words, “teach or explain to” - the system to be able to perform at later, improved levels; the machine learns to do that on its own after initial programming.
What is deep learning then?
Deep learning, as discussed earlier, is machine learning. Just, more advanced or evolved. It is a subset of machine learning that involves many more layers of algorithms compared to what machine learning uses.
This layered structure is called an artificial neural network (ANN). These several layers of algorithms are similar to - and modelled on - the brain and millions of neuron connections in it. This is why we refer to them as artificial neural networks.
These multiple layers allow this learning process to be more optimized, refined and accurate. Artificial neural networks that machines use, allow them to make decisions without human intervention.
Deep learning models generally have two or more hidden layers of algorithms. Thus, a machine ultimately is able to draw conclusions the way humans do.
A novel application of deep learning is being developed at advanced tech startup Tooliqa, which would allow interior designers to reconstruct interior spaces in 3D. It is a radical approach to interior design using deep learning algorithms that greatly enhance efficiency by saving valuable time and money.
This approach is similar in intelligent sensing technological companies like Cipia and Verkada.
What are the differences between Machine learning and Deep learning?
We have established that deep learning is essentially machine learning but let’s outline the subtle differences between the two.
The need for ongoing human intervention
Machine learning models become better at each step because the data that is fed into the system acts as a training model as it continues to learn and improve its performance on its own.
However, they need some human intervention.
The programmer or software engineer needs to also feed mostly structured data into the system to train it to draw patterns.
Once programmed, the machine can take infinite data points to analyze and draw results.
If there is some error or inaccuracy in the predictions or results drawn by the AI algorithm, then a programmer needs to step in and fix it. This indicates a greater need for ongoing human intervention.
However, deep learning does not need this kind of intervention as the algorithms can decide for themselves if a prediction is accurate or not.
Deep learning algorithms draw interpretations and predictions automatically and the machine can correct its mistakes without the need for a programmer to do so.
Another major difference is the amount of data input they require.
Since most traditional machine learning models do not involve several layers of algorithms, they require a relatively smaller data input.
Deep learning requires vast amounts of data to rule out inaccuracies and derive accurate interpretations.
Machine learning models require thousands of data points, whereas deep learning models require millions.
This is tied to the complexities of both learning models. Machine learning, with lesser data input and not-so-complex algorithms, has a simpler structure compared to deep learning, whose algorithm layers are modelled on the human brain.
Computation and Hardware
Because of the vast data required, deep learning takes more time to train neural networks and make accurate predictions. Machine learning, on the other hand, requires less computing power and allows one to train the machine comparably quickly.
Machine learning programs can often run on conventional computers due to their relative simplicity and a smaller amount of data. Deep learning requires very powerful hardware to function efficiently.
Deep learning algorithms have the capability to train themselves. This is why the results are highly accurate and delivered much faster than machine learning.
For machine learning there is manual intervention, there could be human errors at times. This reduces the efficiency and accuracy of the results.
Due to the high accuracy of predictions and minimal range of errors presented through the self-training models, engineers prefer deep learning over machine learning.
As we transition into times where self-driving cars and serving robots are becoming an everyday reality, we are learning to appreciate and admire all that advanced technology can do for us.
Read also: Deep Learning and AI: Applications | Insights - Tooliqa
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