At first glance, it might seem that machine learning and deep learning are the same thing. After all, they’re both hot topics in artificial intelligence right now, and they both can do some pretty cool things with data. But while they definitely share some common ground, the two are quite different from one another—so it’s important to understand the differences between machine learning and deep learning in order to make sure you use each to its full potential.
What's the difference between machine learning and deep learning?
Machine learning is the process through which computers use algorithms to learn from data and carry out tasks without being explicitly programmed. Deep learning relies on sophisticated algorithms that were modeled after the human brain. This makes it possible to process unstructured data, including text, photos, and documents.
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To put it simply, deep learning is a specialized branch of machine learning, which is itself a branch of artificial intelligence. Deep learning is machine learning, in other terms.
But let's explore a bit more deeply.
Table Of Contents
- What's the difference between machine learning and deep learning?
- What Is Machine Learning?
- Types of Machine Learning
- How does Machine learning Work?
- What is deep learning?
- How does Deep learning work?
- Different types of Deep learning Algorithms
- Key Differences Between Machine Learning and Deep Learning
- Conclusion
What Is Machine Learning?
Machine learning is a subset of artificial intelligence that deals with the construction and study of algorithms that can learn from and make predictions on data. These algorithms are able to automatically improve given more data.
It is closely related to computational statistics, which focuses more on statistical modeling of large data sets. These models can be used for a variety of purposes, including classification and prediction. Classification is used to determine what category something belongs in, while prediction is forecasting an expected value or result. Machine learning has a number of applications including business analytics, fraud detection, and natural language processing.
Types of Machine Learning
Machine learning comes in three flavors:
- Supervised,
- Unsupervised, and
- Reinforcement.
Supervised learning:
Supervised learning is where the data is labeled and the algorithm is told what to do.
Consider the scenario where a programmer is attempting to "teach" a computer how to distinguish between dogs and cats. A set of labeled data, in this case images of cats and dogs with their names plainly visible, would be sent to the computer model. The model would eventually begin to recognize patterns, such as the fact that dogs can smile and cats have long whiskers. The model's capacity to correctly recognize dogs and cats would then be tested by feeding the computer unlabeled data (unidentified photographs).
Unsupervised learning:
When data isn't labeled and the algorithm is left to uncover patterns on its own, this is known as unsupervised learning.
Consider a scenario in which your company wishes to examine data to pinpoint specific client categories. However, you aren't yet aware of the segments. The unsupervised learning model must be fed the unlabeled input data in order for it to function as a classifier of customer segments on its own.
Reinforcement Learning:
Reinforcement learning is where an agent interacts with its environment to learn what to do.
By receiving feedback from its own behaviors, a model can learn using the reinforcement learning method, which is a trial-and-error process. When the computer correctly recognizes or categorizes data, it receives "positive feedback," whereas it also receives "negative feedback" when it doesn't. This approach of teaching strengthens the former by "rewarding" good behavior and "punishing" bad behavior. (And it distinguishes reinforcement learning from supervised learning, in which a data scientist simply validates or corrects the model instead of rewarding or penalizing it.)
How does Machine learning Work?
Understanding how machine learning models work can be done by looking at an image of a car or men. The ML model uses photos of both car and men as input to extract various properties like shape, height, nose, and eyes, then applies the classification algorithm to the extracted features to predict the outcome. The below picture is displaying the working process of ML:
What is deep learning?
Artificial neural networks, a class of algorithms inspired by the structure and operation of the brain, are the focus of the deep learning subfield of machine learning. A group of algorithms called neural networks are created to recognize patterns. They categorize or group raw input to understand sensory data using a form of machine perception.
Deep learning is a branch of machine learning that takes cues from the way the brain is organized and how it performs certain tasks. Algorithms for deep learning are built to learn in a hierarchical fashion, just like people do. As an illustration, we start with basic shapes like circles and squares when we are first learning to recognize items. The ability to recognize more complex objects, such as people and animals, increases with experience. Deep learning algorithms first pick up on fundamental ideas before expanding on them to create more intricate understandings.
How does Deep learning work?
With the same example of differentiating between a car and a men, we can comprehend how deep learning functions. Without the need for a human feature extraction stage, the deep learning model uses the photos as its input and feeds them straight to the algorithms. The photos are passed along to the various artificial neural network layers, which forecast the outcome.
Watch the below Picture:
Different types of Deep learning Algorithms
There are different types of deep learning algorithms, each with its own use case.
For example, some are used for image recognition, while others are used for text classification. Some of the most popular deep learning algorithms include
Convolutional Neural Networks (CNNs)
CNNs are often used in computer vision applications, such as self-driving cars or mobile phones that can capture images from a camera and tell you what is in them.
Recurrent Neural Networks (RNNs)
RNNs have shown promising results in natural language processing tasks like machine translation or speech synthesis.
Key Differences Between Machine Learning and Deep Learning
Taking a look at the key differences between machine learning and deep learning based on different parameters:
| Parameters | Machine Learning | Deep Learning |
|---|---|---|
| Data Dependency | Even though machine learning requires a vast quantity of data, it can function with less data. | For Deep Learning algorithms to perform well, we must feed them with a lot of data because they heavily rely on it. |
| Hardware Dependencies | Machine learning models can be used on low-end machines because they don't require a lot of data. | Because the deep learning model requires a large amount of data to operate effectively, a high-end system with GPUs is required. |
| Execution time | While testing a model with a machine learning algorithm takes a lengthy time, it takes less time to train the model than deep learning does. | Deep Learning requires a lot of processing time during model training but less during model testing. |
| Feature Engineering | Before moving forward, machine learning models require feature extraction by a professional. | Since deep learning is an improved form of machine learning, it does not require the creation of a feature extractor for every issue but instead attempts to learn high-level features independently from the data. |
| Type of data | Most often, structured data is needed for machine learning models. | As they rely on the layers of the artificial neural network, deep learning models may deal with both organized and unstructured input. |
| Approach to tackling problems | The conventional ML paradigm divides a problem into smaller components and then solves each component in turn to arrive at the final solution. | A deep learning model approaches problems differently than a typical ML model since it accepts input for a specific problem and produces the solution. As a result, it employs an end-to-end strategy. |
| Result interpretation | For a specific problem, the result is simple to interpret. Because we can simply analyze the results when using machine learning, it is clear why a particular result occurred and how the process worked. | It is exceedingly difficult to interpret the results for a specific problem. As an example, while using a deep learning model, we may obtain a better solution than a machine learning model for a particular issue, but we are unable to determine why this specific result occurred. |
| Appropriate for | Machine learning models can be used to solve straightforward or a little bit challenging issues. | Deep learning models can be used to solve more challenging issues. |
Conclusion
In conclusion, deep learning can be defined as machine learning that has additional capabilities and a unique method of operation. The amount of data and intricacy of the problem must be taken into account while choosing one of them to solve it.
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