Computational models with multiple processing layers can learn data representations at different abstraction levels through deep learning. These methods have considerably improved the situation in numerous other domains, such as drug discovery, genomics, object identification, visual object recognition, and speech recognition.1    

So, what does deep learning mean?

In Artificial Intelligence (AI) and machine learning, deep learning models represent a new paradigm of learning. Algorithms for deep learning are based on neural network models that replicate the structure and operation of the brain.2 They allow systems to cluster data and produce incredibly accurate predictions. Many AI apps and services are powered by deep learning, which enhances automation by carrying out mental and physical tasks without the need for human intervention. Voice-activated consumer electronics, digital assistants, and upcoming technologies like self-driving cars are all powered by deep learning technology.3

Although neural networks have been successfully employed in numerous applications for a while, interest in this subject later waned. However, the publication of “Deep Learning” by Hinton et al. in 2006 brought it back into the spotlight, reviving interest in neural network research. 4

How Does Deep Learning Work?

Deep learning neural networks are artificial neural networks that attempt to imitate the human brain by incorporating data inputs, weights, and biases. Deep learning networks learn by spotting intricate patterns in the material they analyze. The networks can generate various abstraction levels to represent the data by constructing computational models made up of multiple processing layers. Together, these components accurately identify, categorize, and describe objects in the data.3,5 

To dive deeper into neural networks, read What Does a Neural Network in Artificial Intelligence Mean?

Deep learning

How, then, is deep learning different from machine learning?

There is a fundamental difference between deep learning and conventional machine learning. Deep learning differs from traditional machine learning in terms of the kind of data it uses and its learning techniques. Machine learning algorithms utilize structured, labeled data to make predictions; therefore, the model’s input data is used to determine specific attributes that are then organized in tables. However, this does not necessarily mean it does not use unstructured data. It implies that, in the event that it does, data will usually process it to give it structure.6

Some data pre-processing that is usually required with machine learning is eliminated by deep learning. These algorithms can process text and image-based unstructured data and automate feature extraction, reducing the need for human intervention. Deep learning systems can outperform regular machine learning systems significantly in various tasks such as speech recognition, computer vision, machine translation, and robotics. 6,7

Deep Learning Applications

Nowadays, deep learning applications are ubiquitous, but they are often so flawlessly incorporated into products and services that customers are oblivious to the complex data processing behind them. We are in an era of incredible potential, and deep learning technology can aid in making fresh discoveries. New drugs, exoplanet discovery, disease detection, and the identification of subatomic particles have all been made possible by deep learning. Bioinformatics, proteomics, metabolomics, the immune system, and more are being fundamentally enhanced by deep learning. Every day, deep learning strides toward improving many aspects of our lives. 8 

If you found this article interesting and want to learn more about coding, visit BYJU’s FutureSchool Blog, where you can find various amazing articles on coding and its applications.

References

  1. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions | Journal of Big Data | Full Text. (n.d.). Retrieved October 12, 2022, from https://journalofbigdata.springeropen.com/articles/10.1186/s40537-021-00444-8 
  2. (PDF) DEEP LEARNING: A REVIEW. (n.d.). Retrieved October 12, 2022, from https://www.researchgate.net/publication/318447392_DEEP_LEARNING_A_REVIEW 
  3. What Is Deep Learning? | How It Works, Techniques & Applications – MATLAB & Simulink. (n.d.). Retrieved October 12, 2022, from https://www.mathworks.com/discovery/deep-learning.html 
  4. An Introductory Review of Deep Learning for Prediction Models With Big Data – PMC. (n.d.). Retrieved October 12, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7861305/ 
  5. What is Deep Learning and How does it work? | Towards Data Science. (n.d.). Retrieved October 12, 2022, from https://towardsdatascience.com/what-is-deep-learning-and-how-does-it-work-2ce44bb692ac 
  6. Deep Learning vs. Machine Learning – What’s The Difference? (n.d.). Retrieved October 12, 2022, from https://levity.ai/blog/difference-machine-learning-deep-learning 
  7. What Is Deep Learning? | Built In. (n.d.). Retrieved October 12, 2022, from https://builtin.com/machine-learning/what-is-deep-learning 
  8. 20 Deep Learning Applications in 2022 Across Industries | Great Learning. (n.d.). Retrieved October 12, 2022, from https://www.mygreatlearning.com/blog/deep-learning-applications/ 
Coding, Learn with Coding