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Difference between Deep Learning and NLP
Deep Learning and Natural Language Processing (NLP) are two of the most popular buzzwords in the industry today. Just like the majority of other great ideas, the concepts underlying NLP have been embraced by a large number of industry leaders. NLP is an area in artificial intelligence that focuses on the interactions that take place between computers and human languages. This investigation into the workings of the human mind is a ground-breaking contribution to the field.
NLP is the study of exactly what goes on in our heads while we think. The University of California, Santa Cruz was the birthplace of NLP in the early 1970s; nevertheless, the field has seen tremendous expansion since that time.
Deep Learning, on the other hand, is a subfield that falls under the umbrella of machine learning and is predicated on the use of artificial neural networks. It is a method of machine learning that instructs computers to learn by modelling themselves after the functioning of the human brain.
What is Deep Learning?
Deep Learning is a subfield of Machine Learning that models the operations of the human brain using computer programs known as Artificial Neural Networks (ANNs). Because an artificial neural network is composed of dozens or millions of neurons that are arranged in layers upon layers, the term "Deep Learning" was coined to describe this type of learning.
The operation of a neural network can be conceptualized as follows − first, huge amounts of data are inputted into the network, and then the data is processed by the network's neurons. An activation function is present in each neuron. When a predetermined limit is breached, the neurons in the neural network become active, and the values stored in them are broadcast to the rest of the network.
ANNs are intended to function in a manner analogous to the ways in which the biological brain processes information and engages in distributed communication. On the other hand, in contrast to the analogous and ever-changing nature of the real brain, artificial neural networks (ANNs) are completely digital and unchanging.
In deep learning, the primary emphasis is placed on the training of huge neural networks using vast amounts of data. Because the amount of data that is being generated on a daily basis around the globe is currently off the charts (and is only going to continue to rise in the years to come), deep learning presents a fantastic opportunity. This is due to the fact that the performance of an extended neural network improves in proportion to the amount of data that is fed into it.
The fields of Predictive Analytics, Natural Language Processing, Computer Vision, and Object Recognition all make substantial use of Deep Learning.
What is Natural Language Processing (NLP)?
Processing of natural languages is a collection of techniques that make it possible for computers to understand human language. The theory that underpins NLP postulates that all human thought revolves around one of the following five senses − sight, sound, sensation, smell, and/or taste. It is an essential component of artificial intelligence that tries to simulate the cognitive processes that are responsible for the comprehension and production of human languages.
The study of natural language processing is the investigation of how computers can process or interpret human languages in order to carry out meaningful tasks. We tend to have a scientific understanding of language in this day and age since we are constantly trying to communicate with machines and other inanimate objects. As a consequence of this, the development of technologies that allow language to be supplied to inanimate objects such as computers has become an absolute necessity. The use of NLP can assist with the same. To put it another way, NLP is a technique that helps computers understand human language.
The following are a few applications of natural language processing −
- Sentiment Analysis
- Information extraction
- Information retrieval
Difference between Deep Learning and NLP
Deep learning is a subfield of machine learning that is founded on the concept of artificial neural networks that instructs computers to learn through observation and repetition. It is a function of artificial intelligence that mimics the way the human brain processes data and creates patterns for the purpose of decision making.
On the other side, natural language processing is a collection of techniques that make it possible for computers to understand human language. It looks at the possibility of employing computers to process or understand human languages for the aim of carrying out tasks that are of practical utility.
Functions of Deep Learning and NLP
The supervised learning process can benefit greatly from deep learning's robust structure. A deep network can express functions of increasing complexity by increasing the number of layers as well as the number of units inside each layer. It is a function of artificial intelligence that simulates the learning and thinking process of humans in order to comprehend data that is unstructured as well as unlabeled.
The interaction between computers and human language is what is known as natural language processing (NLP). It looks at the possibility of employing computers to process or understand human languages for the aim of carrying out tasks that are of practical utility. The goal is to read, decipher, and comprehend the various human languages in a way that is useful.
Applications of Deep Learning and NLP
When it comes to text classification and organisation, NLP can be applied in a variety of different methods to achieve the desired results. Text classification is helpful in a wide variety of applications, including information filtering, web searching, readability analysis, and opinion mining.
Other uses include market intelligence, customer service, chatbots, automatic summarization, automatic speech recognition, machine translation, and more.
The language translation services offered by Google, Alexa, and selfdriving cars all make use of an algorithm called deep learning.
Drug development, voice synthesis, and facial identification and recognition are three further fields that rely substantially on deep learning.
The following table highlights the major differences between Deep Learning and NLP −
| Basis of Comparison | Deep Learning | NLP |
|---|---|---|
| Definition | Deep learning is a subfield of machine learning that is founded on the concept of artificial neural networks that instructs computers to learn through observation and repetition. | The ability of a computer software to understand human language in its natural, spoken form is referred to as "natural language processing." |
| Function | It is a function of artificial intelligence that mimics the way the human brain processes data and creates patterns for the purpose of decision making. | It looks at the possibility of employing computers to process or understand human languages for the aim of carrying out tasks that are of practical utility. |
| Process | In order to handle material that is both unstructured and unlabelled, an AL function was developed that simulates the learning and reasoning process that occurs in humans. | The interaction between computers and human language is what is known as natural language processing (NLP). |
| Application | The language translation services offered by Google, Alexa, self-driving cars, speech synthesis, facial recognition, and other technologies all make use of deep learning algorithms. | Machine translation, automatic summarization, automatic speech recognition, chatbots, market intelligence, customer service, and other applications are all possible uses of this technology. |
Conclusion
Deep learning is a collection of techniques that are based on artificial neural networks that are designed to mimic the human brain. These techniques enable computers to learn from data without the need for human supervision or intervention. In addition to this, these strategies are flexible enough to adjust to shifting conditions and ensure the ongoing development of newly acquired skills.
Natural Language Processing is a subfield of artificial intelligence that focuses on the interactions that occur between computers and human languages. The ability of a computer software to interpret human language in its spoken form is known as natural language processing. It is one of the most notable technologies to emerge during the information age.
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