How Machine Learning and Deep Learning Differ from One Another?
In recent years, two subfields of artificial intelligence (AI) have attracted a lot of attention and popularity: Machine Learning and Deep Learning. The creation of algorithms that enable computers to learn from data and carry out activities that ordinarily call for human intellect is the focus of both of these topics.
Deep learning and Machine learning have several significant distinctions that are worth considering despite their similarities. One of the best options to learn Machine Learning is from Intellipaat, you can enroll in a Machine Learning Certification course.
Let’s discuss Deep learning and machine learning as they have several significant distinctions that are worth considering despite their similarities.
Given below are the following topics we are going to cover in this post:-
- Machine Learning vs Deep Learning
- Difference between Machine Learning vs Deep Learning
- Application of Machine Learning and Deep Learning
- Conclusion
Machine Learning vs Deep Learning
Even while the phrases "deep learning" and "machine learning" are sometimes used interchangeably, There are quite a good number of top technologies to learn but Machine Learning and Deep Learning are actually quite distinct. Despite the fact that they both pertain to artificial intelligence, machine learning is essentially a subset of deep learning.
Deep learning, a form of machine learning, employs numerous layers of neural networks to learn from data. Machine learning is the process of utilizing algorithms to evaluate data and generate predictions or judgments. Let’s discuss each of them one by one to have a better understanding:
A branch of AI known as "Machine Learning" focuses on creating algorithms that let computers learn from data and make predictions or judgments.
Generally speaking, there are three different types of machine learning algorithms: reinforcement learning, unsupervised learning, and supervised learning.
In supervised learning, an algorithm is trained using a labeled dataset and then predictions are made using the training data. Unsupervised learning requires that the algorithm find patterns and correlations in the data on its own, without the use of a labeled dataset.
With reinforcement learning, an algorithm is trained via trial and error and given feedback in the form of rewards or penalties.
Contrarily, Deep Learning is a branch of machine learning that focuses on creating artificial neural networks.
These networks, which are modeled after the structure and operation of the human brain, are able to learn from data and form conclusions based on intricate patterns and correlations.
Deep learning techniques may be utilized for a range of tasks, including speech recognition, picture identification, and natural language processing.
Difference between Machine Learning vs Deep Learning
In the field of artificial intelligence, the terms "machine learning" and "deep learning" are frequently used. Although they are commonly used as synonyms, they are not the same. Let's look at the primary differences between machine learning and deep learning in tabular form to comprehend them better:
| Machine Learning | Deep Learning | |
|---|---|---|
| Definition | Algorithms that can learn from data without being explicitly programmed are the focus of this branch of AI. |
A branch of machine learning that uses multi-layered artificial neural networks for learning and decision-making. |
| Algorithms | Include k-nearest neighbors, SVM, Naive Bayes, decision trees, random forests, and so on. |
Includes Generative Adversarial Networks (GAN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), etc. |
| Input | Structured or semi-structured data. | Structured, unstructured, and image data. |
| Decision Making | Based on patterns and relationships in the data. |
Based on patterns and relationships learned from large amounts of data through multiple layers of neural networks. |
| Learning Process | Supervised, unsupervised, and reinforcement learning. |
Usually supervised, but can also be unsupervised or semi-supervised. |
| AUse Cases | Detecting spam, detecting credit card fraud, forecasting the stock market, etc. |
Natural language processing, audio recognition, image recognition, etc. |
Application of Machine Learning and Deep Learning
Artificial intelligence (AI) has transformed several sectors by enhancing decision-making processes through the use of two well-known subfields: machine learning (ML) and deep learning (DL). First, we would like to see the major uses of ML across several fields:
Healthcare
- Disease diagnosis and prognosis: Based on patient data, including medical pictures, test results, and demographic data, ML algorithms are used to identify illnesses and forecast how they will develop over time.
- Personalized medicine: DL algorithms may examine a significant quantity of genetic data to determine the best courses of action for specific individuals.
Finance
- Fraud detection: By examining trends in transaction data, ML systems may spot fraudulent transactions.
- Credit scoring: By examining a borrower's financial history and other pertinent information, DL algorithms can forecast their creditworthiness.
Marketing
- Customer segmentation: By classifying customers into categories based on their purchase patterns, ML systems help marketers better target their advertising efforts.
- Systems for recommending goods or services: DL algorithms can suggest goods or services to clients based on their earlier behavior and preferences.
Manufacturing
- Predictive maintenance: ML algorithms can predict when equipment is likely to fail, allowing manufacturers to schedule maintenance and minimize downtime.
- Quality control: DL algorithms can scan items for flaws and pinpoint the source of the issue, enabling producers to raise the standard of their goods.
Now let’s see the various application of Deep Learning in the below section:
- Computer Vision: Deep Learning algorithms can be used to recognize objects, detect faces, perform segmentation, and more. Convolutional Neural Networks (CNNs) are widely used in computer vision applications.
- Natural Language Processing (NLP): Deep Learning algorithms can be used to perform text classification, sentiment analysis, machine translation, and more. Recurrent Neural Networks (RNNs) and Transformers are widely used in NLP applications.
- Speech Recognition: Deep Learning algorithms can be used to transcribe speech to text, perform speaker recognition, and more. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are widely used in speech recognition applications.
- Robotics: Deep Learning algorithms can be used to develop robots that can perform various tasks, such as object recognition, grasping, and navigation. Reinforcement Learning is often used in robotics applications.
- Gaming: Deep Learning algorithms can be used to develop game AI agents that can learn to play games and make decisions. Reinforcement Learning is often used in gaming applications.
Conclusion
There are many parallels and contrasts between the two main artificial intelligence subfields of machine learning and deep learning.
Deep learning focuses on creating artificial neural networks that can learn from intricate patterns and correlations in data, whereas machine learning is concerned with creating algorithms that let computers learn from data and make predictions. The decision between machine learning and deep learning will be based on the particular issue at hand since both techniques have advantages and disadvantages.