Difference Between NLP NLG NLU AI MI And DL Training Ppt
These slides draw a comparison between NLP Natural Language Processing, NLG Natural Language Generation, NLU Natural Language Understanding, Artificial Intelligence AL, Machine Learning ML, and Deep Learning DL.
These slides draw a comparison between NLP Natural Language Processing, NLG Natural Language Generation, NLU Natural Langua..
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Content of this Powerpoint Presentation
Slide 1
This slide draws a comparison between Natural Language Processing (NLP), Natural Language Understanding (NLU) and Natural Language Generation (NLG)
Instructor Notes:
- NLP (Natural Language Processing): It comprehends the meaning of the text
- NLU (Natural Language Understanding): NLU handles whole processes such as choices and actions
- NLG (Natural Language Generation): This method creates human language text from structured data provided by the system to reply
Slide 2
This slide lists the difference between Artificial Intelligence (AI) and Machine Learning (ML). AI is a technology that simulates a machine to replicate human behavior. Machine Learning is a kind of AI in which a machine learns from previous data without being explicitly programmed.
Slide 3
This slide states that Machine Learning is a kind of Artificial Intelligence that uses statistical learning algorithms to construct systems that can understand and evolve on their own without being explicitly programmed. Deep learning is an ML approach boosted by how the human brain filters information; it is essentially learning by example. It enables a computer model to anticipate and categorize information by filtering input data through layers.
Slide 4
This slide states a comparison between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). Artificial Intelligence is the process of combining machines with human intelligence. Machine Learning is concerned with teaching machines to learn by training algorithms on reams and reams of data. Deep Learning is a kind of Machine Learning that focuses on data representations rather than task-specific methods.
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