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Deep Learning Vs NLP: Difference Between Deep Learning & NLP

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14th Sep, 2020
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Deep Learning Vs NLP: Difference Between Deep Learning & NLP

When we think of Artificial Intelligence, it becomes almost overwhelming to wrap our brains around complex terms like Machine Learning, Deep Learning, and Natural Language Processing (NLP). After all, these new-age disciplines are much more advanced and intricate than anything we’ve ever seen. This is primarily why people tend to use AI terminologies synonymously, sparking a debate of sorts between different concepts of Data Science

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One such trending debate is that of Deep Learning vs. NLP. While Deep Learning and NLP fall under the broad umbrella of Artificial Intelligence, the difference between Deep Learning and NLP is pretty stark!

In this post, we’ll take a detailed look into the Deep Learning vs. NLP debate, understand their importance in the AI domain, see how they associate with one another, and learn about the differences between Deep Learning and NLP. 

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So, without further ado, let’s get straight into it!

Also, check out our free NLP course 

Deep Learning vs. NLP

What is Deep Learning?

Deep Learning is a branch of Machine Learning that leverages artificial neural networks (ANNs)to simulate the human brain’s functioning. An artificial neural network is made of an interconnected web of thousands or millions of neurons stacked in multiple layers, hence the name Deep Learning.

A neural network functions something like this – you feed the neural network with massive volumes of data that will then run through the neurons. Each neuron has an activation function. When a specific threshold is reached, the neurons get activated, and their values are disseminated throughout the neural network.

ANNs are designed to imitate the information processing and distributed communication approaches of the biological brain. However, they differ from the biological brain in the sense that while the biological brain is analog and dynamic, ANNs are static. 

Deep Learning focuses on training large neural networks on voluminous amounts of data. Since the daily global data generation is off the charts right now (and it will only increase in the future), it presents an excellent opportunity for Deep Learning. This is because the more data you feed into an extensive neural network, the better it performs.  

Deep Learning is extensively used for Predictive Analytics, NLP, Computer Vision, and Object Recognition. 

Must Read: Top 10 Deep Learning Techniques You Should Know

What is Natural Language Processing?

Natural Language Processing is an AI specialization area that seeks to understand and illustrate the cognitive mechanisms that contribute to understanding and generating human languages. In essence, NLP is a confluence of Artificial Intelligence, Computer Science, and Linguistics. Through the intelligent analysis of natural human languages, NLP aims to bridge the gap between computer understanding and natural human languages. 

NLP focuses on programming computers to process and analyze large amounts of natural language data in the textual or verbal forms. It uses advanced methods drawn from Computational Linguistics, AI, and Computer Science to help computers understand, interpret, and manipulate human languages. As NLP opens communication lines between computers and humans, we can achieve exceptional results like Sentiment Analysis, Information Extraction, Text Summarization, Text Classification, and Chatbots & Smart Virtual Assistants. 

Also Read: Applications of Natural Language Processing

Deep Learning vs. NLP: A detailed comparison


Deep Learning is an ML specialization area that teaches computers to learn from large datasets to perform specific tasks. It uses ANNs to mimic the biological brain’s processing ability and create relevant patterns for informed decision making. 

On the contrary, NLP primarily deals in facilitating open communication between humans and computers. The aim here is to make human languages accessible to computers in real-time. 


Deep Learning uses supervised learning to train large neural networks using unstructured and unlabeled data. Since a deep neural network consists of multiple layers and numerous units, the underlying processes and functions are incredibly complex. Training neural networks aim to help them achieve mastery over specific tasks that usually require human intelligence. 

NLP is concerned with how computers can process, analyze, and understand human languages. It makes use of diverse techniques such as statistical methods, ML algorithms, and rule-based approaches. Using these methods, NLP breaks down natural languages into shorter elements, tries to understand the relationships between these pieces, and explores how they fit together to create meaning.


Deep Learning technology has found application across several industry sectors, including healthcare, BFSI, retail, automotive, and oil & gas, to name a few. It is the technology behind deep dreaming, autonomous cars, visual recognition systems, and fraud detection software. 

NLP is deeply rooted in linguistics. Some of its most popular applications include text classification & categorization, named entity recognition, parts-of-speech tagging, semantic parsing, paraphrase detection, spell checking, language generation, machine translation, speech recognition, and character recognition. These are indispensable in the making of chatbots, personal assistants, grammar and spell checkers, etc. 

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Wrapping up

As we mentioned earlier, Deep Learning and NLP are both parts of a larger field of study, Artificial Intelligence. While NLP is redefining how machines understand human language and behavior, Deep Learning is further enriching the applications of NLP. Deep Learning and vector-mapping techniques can make NLP systems much more accurate without heavily relying on human intervention, thereby opening new possibilities for NLP applications.

Checkout upGrad’s Advanced Certificate Programme in Machine Learning & NLP. This course has been crafted keeping in mind various kinds of students interested in Machine Learning, offering 1-1 mentorship and much more.


Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1What is the difference between deep learning and NLP?

NLP stands for Natural language processing which is the branch of artificial intelligence that enables computers to communicate in natural human language (written or spoken). NLP is one of the subfields of AI. Deep learning is a subset of machine learning, which is a subset of artificial intelligence. As a matter of fact, NLP is a branch of machine learning - machine learning is a branch of artificial intelligence - artificial intelligence is a branch of computer science. Deep learning is a subset of machine learning.

2Is NLP required in deep learning networks?

No. Deep learning algorithms do not use NLP in any way. NLP stands for natural language processing and refers to the ability of computers to process text and analyze human language. Deep learning refers to the use of multilayer neural networks in machine learning. Neural networks have been successfully used in areas other than natural language processing such as object recognition and visual object tracking. Although deep learning algorithms are not based on NLP, they have greatly improved the performance of computers in recognizing images and classifying objects.

3What is NLP and what are its applications?

Natural language processing is the research of computer systems that handle the processing of human language data. It is a relatively new science which has gained enormous popularity because of its application to practical problems in industry, science, and engineering. Problems in this field are still very difficult to solve. Natural language processing is a multi-disciplinary task that involves knowledge in fields such as computational linguistics, artificial intelligence, machine learning, logic programming, mathematics, philosophy, and cognitive science.

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