Deep learning language models represent a class of artificial intelligence (AI) models that leverage deep neural networks to understand and generate human-like language. These models have significantly advanced natural language processing (NLP) capabilities, allowing them to comprehend and generate text with a level of sophistication that was previously challenging to achieve. Fundamental Architecture: Deep learning language models are built upon neural networks, specifically recurrent neural networks (RNNs) or transformer architectures. The core idea is to create a network with multiple layers, allowing the model to learn hierarchical representations of language. Each layer processes information from the previous layer, enabling the model to capture intricate patterns and dependencies within the data. Embeddings: Language models typically begin by representing words as embeddings. These embeddings encode semantic information about words and enable the model to understand relationshi

  Natural Language Processing (NLP)   refers to AI method of communicating with an intelligent systems using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc.The field of NLP involves making computers to perform useful tasks with the natural languages humans use. The input and output of an NLP system can be Speech Written Text Components of NLP : There are two components of NLP as given − 1. Natural Language Understanding (NLU) Understanding involves the following tasks − Mapping the given input in natural language into useful representations. Analyzing different aspects of the language. 2. Natural Language Generation (NLG) It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation. It involves − Text planning  − It includes