What Are The Advantages Of Deep Learning Over Machine Learning?4.9 out of 5 based on 1462 votes
Last updated on 21st Oct 2022 7.5K Views
In the recent era, Deep Learning is one of the most advanced and popular technologies. It is much more efficient than Machine Learning and can be useful in a number of tasks.
Deep Learning is a subset of machine learning that consists of Artificial Neural Networks (ANNs) that are layered. Neural Networks refer to the networks of neurons or nodes – algorithmic locations where the computation of inputs takes place. In addition, each layer in this technology consists of a certain number of neurons. These neurons receive input, compute it, and forward the output to the next layer until it reaches the final layer. To further know about its functioning, one can visit the Deep Learning Online Course.
Applications of Deep Learning
Deep Learning is one of the most advanced and popular technologies in today's era. It is much more efficient than Machine Learning and can be useful in a number of tasks. This technology is based on learning and improving on its own by examining computer algorithms. Given below are some of the significant applications of Deep Learning.
- Personalized Marketing-Customized deep learning models help in interpreting data from different sources to extract valuable customer insights. This information helps them in predicting consumer behavior and target their marketing efforts more efficiently.
- Facial Recognition- It can identify individuals from images and videos by documenting their faces. It enforces Facial Recognition by recording face embeddings and using a trained model to map them against a huge database.
- Recommendation Systems-Deep Learning models are capable of processing user data and compiling them to extract consumer info. After this task, it sends the information into deep learning-based recommender systems to generate appropriate suggestions for the users.
- Smart Agriculture- Using this technology in agriculture helps in increasing resilience to natural phenomena and diseases. In addition, it also improves crop yield per unit area and helps in breeding high-quality hybrids.
- Space Travel-Space Travel is one of the most important subjects in this technological era. Therefore, scientists and engineers use the latest and most efficient technologies like Deep Learning to ensure the safety, integrity, and success of space missions.
Advantages of Deep Learning over Machine Learning
Traditional machine learning refers to the process of extracting knowledge from a large dataset loaded into the machine. In this process, the user provides the machine with training data and examples to help it make correct decisions. Machine Learning technology is capable of solving a significant number of tasks, but it cannot perform them without human control. On the other hand, Deep learning is much more advanced than Machine Learning, and it is capable of creating new features by itself. This technology solves problems on an end-to-end basis, while machine learning divides tasks into smaller pieces. Many institutes provide Deep Learning Training in Delhi and one can enroll in them to start a career in it. Apart from these, given below are some of the advantages of Deep Learning over Machine Learning.
- Feature Generation Automation-Deep learning algorithms are capable of generating new features from training datasets without additional human intervention. This feature allows deep learning to perform complex tasks that often require extensive feature engineering.
- Works Well with Unstructured Data- It is capable of working with unstructured data and the majority of business data is unstructured. In addition, most of the unstructured data used in business are Text, images, and voice.
- Better Self-Learning Capabilities- It has self-learning capabilities that make you more efficient at learning complex features. These capabilities make Deep learning much more efficient than machine learning in the ability to make sense of inputs.
- Cost Effectiveness- Training deep learning models is expensive, however once trained it can help businesses cut down on unnecessary expenditure. In addition, these algorithms can factor in variation across learning features to reduce error margins dramatically across industries.
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