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In today’s digital world, where data is abundant, Artificial Intelligence (AI) plays a crucial role. Under the broad umbrella of AI, there is a specific and powerful technology called Deep Learning. In this blog post, we will explore Deep Learning and answer the following questions: Is Deep Learning artificial intelligence? How does Deep Learning work? How does Deep Learning differ from machine learning? And why is Deep Learning so important?
Deep Learning (DL) is a part of Machine Learning (ML), which in turn is a subfield of Artificial Intelligence (AI). Thus, DL is a subfield of AI that specializes in how computers can learn in ways similar to the human brain to solve problems or complete tasks. It uses special mathematical models called “artificial neural networks” to process information and recognize patterns in data.
Imagine trying to teach a computer to tell dogs from cats based on photos. Deep Learning would train the computer to automatically recognize the differences between dogs and cats without you having to explicitly tell it which features (such as ear shape or coat pattern) are important.
Let’s take our example with the dogs and cats again to illustrate how exactly Deep Learning works.
So Deep Learning is like teaching a computer to recognize patterns in data by analyzing and learning from it on its own. This makes it possible to automate complex tasks, such as recognizing faces, translating languages, or predicting stock prices. It is an exciting technology that is being used in many fields to automate tasks and mimic human intelligence.
Machine Learning (ML) and Deep Learning are closely related, yet differ in a few ways:
| Deep Learning | Machine Learning | |
|---|---|---|
|
Architecture and Model Complexity |
DL uses artificial neural networks (ANNs) with many layers of neurons. These networks are very deep, enabling them to learn high-dimensional and complex patterns in the data without requiring human experts to select features. This leads to higher model complexity.
|
ML algorithms typically rely on processing data and learning patterns based on selected features or properties that are chosen by humans. These models can be complex, but their architecture is typically less deep |
|
Feature engineering |
DL can automatically extract relevant features from raw data, significantly reducing the need for manual feature engineering. |
ML often requires data scientists to extract and select features from the raw data to help the model learn. |
|
Data requirements |
DL models typically benefit from large data sets and tend to perform better when provided with an abundance of training data. |
ML models can often work effectively with smaller data sets and do not always require huge amounts of data. |
|
Computational Resources |
DL models are more computationally intensive and benefit from more powerful hardware resources such as GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). |
ML models generally require fewer computational resources compared to Deep Learning models. They can run on traditional computers or smaller servers. |
|
Applications |
DL is used in applications that require complex pattern recognition, such as image recognition, speech processing, autonomous vehicles, automatic translation, and more. |
ML is often used for tasks such as linear regression, decision trees, support vector machines, and k-means clustering. It is well suited for many classical data-based problems. |
The importance of Deep Learning cannot be overstated. Here are some reasons why it is so important:
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Image credits: Header- & Featured image by  macrovector on Freepik
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