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Relation between deep learning and machine learning
There is a buzz among the students regarding the terms like deep learning and machine learning. Machine learning is a subset of Artificial intelligence and Deep learning is a subset of Machine learning. Although there are many differences between them, many students get confused. This is not wrong to get confused because both these technologies are from a similar field. Choosing a career in either of these fields needs a clear vision and understanding of the topic. In this article, we will discuss the various aspects where machine learning and deep learning are similar and where they are different.
What is Deep Learning
Deep learning uses the concept of neural networks having multiple layers of data which is used to analyze the various complex patterns and relationships between the provided data. Deep learning is somehow inspired by the structure and functionality of human nerves. As the human nerves are used by the brain to analyze the available data and information around them, the same concept is used in the Deep Learning of Artificial Intelligence. Various tasks that can be performed using deep learning are image recognition, natural language processing, etc.
A large amount of data and information along with various algorithms are used to train the deep learning models. These get improved with the passing of time as deep learning automatically corrects itself which increases the efficiency and accuracy of the model. In deep learning, there is a need for a specific GPU (Graphic Processing Unit) for the training of the models. Deep learning needs powerful hardware and background resource so that the training models can be trained well.
What is Machine Learning?
Machine learning is a process of teaching computers to identify patterns in data and make predictions or choices based on those patterns. Large volumes of data are generally sent to the computer to find patterns and make predictions about upcoming, unforeseen data. There are various types of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning. Experts can use each type of machine learning to address different types of problems. Let's discuss the different types of machine learning −
Supervised Learning − It is one of the most common types of machine learning. It requires training a specific model on the labeled dataset where the right outputs are known. Then, that particular model makes guesses on new and unseen data. Regression, decision trees, and support vector machines are instances of supervised learning.
Unsupervised Learning − It requires training a model on the unlabeled set of data where the right output is unknown. The models have to search for patterns or structures in the existing information on their own. Examples of unsupervised learning include clustering and association rule mining.
Reinforcement Learning − This type of machine learning involves training a model to make resolutions in the environment by performing the actions and getting penalties or rewards. The model learns to maximize its rewards over time. This kind of learning is often used in robotics and gaming.
Various Parameters Regarding Deep Learning and Machine Learning
In machine learning, there is a need for human intervention to identify and apply the various features based on their datatype, such as shape, pixel, orientation, etc. But, in deep learning, the training model itself tries to learn these features such as pixel, shape, and orientation without any human involvement.
The amount of data used in deep learning systems are very enormous in size. These data need to be processed efficiently for which there are various complex mathematical calculations needed. And to perform these complex calculations, powerful hardware systems are required. There is a dedicated GPU (Graphical Processing Unit) needed to train deep learning models. On the other hand, machine learning does not need that much powerful hardware as compared to deep learning. Machine learning can be performed using general CPUs (Central Processing Unit)
As we know, there are used datasets required to train a deep learning model. Apart from that, there are various parameters and complex mathematical formulas are utilized to train a deep learning model which takes a lot of time to train. On the other hand, machine learning uses fewer parameters and less complex mathematical formulas as compared to the deep learning model. So, it takes lesser time to train a machine learning model.
The algorithms used in machine learning models first divide data into various parts and then these separated parts are joined together to get a meaningful solution. Whereas, the deep learning model takes the entire scenario in one instance. For example, if a user wants to build a model that will be used to identify objects from an image. The approach to machine learning will be in two steps. The first one will be “object detection” and the second one will be “object recognition”. Taking the same problem in a deep learning program, the input will be the image and the deep learning model will return the identified objects and the location of that particular object in a single result.
Till now, you have gone through the various differences between deep learning and machine learning. All these measures are taken care of while specializing the various applications. Basic predictive programs such as weather forecasting, stock market, spam identifier, etc. are the applications of machine learning. Whereas, The programs that use various layers of neural networks for detection and recognition are applications of deep learning, such as self-driving cars, music, movie streaming services, etc.
Machine learning is a subset of Artificial intelligence and Deep learning is a subset of Machine learning.
Deep learning uses the concept of neural networks having multiple layers of data which is used to analyze the various complex patterns and relationships between the provided data.
Machine learning is a process of teaching computers to identify patterns in data and make predictions or choices based on those patterns. Large volumes of data are generally sent to the computer to find patterns and make predictions about upcoming.
Various types of machine learning are Supervised learning, Unsupervised learning, and Reinforcement learning.
In machine learning, there is a need for human intervention to identify and apply the various features based on their data, but there is less human intervention in the deep learning model.
A deep learning model takes a lot of time to train but a machine learning model takes lesser time to train.
The various applications of machine learning are weather forecasting, stock market, spam identifier, etc and applications of deep learning are self-driving cars, music, movie streaming services, etc.
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