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Tutorial on AI
The RAG model is designed to enhance the capabilities of natural language processing models by combining elements of retrieval-based methods with generative approaches.
RAG is an AI framework that allows a generative AI model to access external information not included in its training data or model parameters to enhance its responses to prompts.
Artificial intelligence and machine learning are two of the hottest topics of discussion in recent times. We get to hear these terms very commonly due to their wide array of applications in different types of industries. Both these technologies are evolving at a great pace. Most importantly, a blend of the two makes it even more powerful. It is precisely the reason why the current trend lies in focusing on an appropriate combination of AI and ML to enhance its potential further. And this makes industries to gain more profits.
The main difference between RNN and CNN come from their structure of the Neural Network. Due to their specific design, CNNs are more fit for spatial data such as images whereas RNNs are more for temporal data that comes in sequence.
CNNs employ filters within convolutional layers to transform data. Whereas, RNNs reuse activation functions from other data points in the sequence to generate the next output in a series.
The main difference between RNN and LSTM is in terms of which one maintain information in the memory for the long period of time. Here LSTM has advantage over RNN as LSTM can handle the information in memory for the long period of time as compare to RNN. But the question is what is different in LSTM than RNN by which LSTMs are capable of maintaining long term temporal dependencies (remembering information for long period of time).
Writing an email is something we do while walking on the road also. The most official mode of communication, So, have you ever seen the ‘Smart Compose’ feature automatically working in your Gmail giving you instant ideas to finish sentences while composing an email? This is one of the different use-instances of language models utilized in Natural Language Processing (NLP). A language model is the core heart of the present day Natural Language Processing (NLP) domain. It’s a measurable device that investigates the example of human language for the forecast of words.
As Machine Learning and Artificial Intelligence (AI) are making their ways in the market there will be a time surely when there will not arise any requirement of developers and IT professionals. The technical bugs will get fixed by the machines themselves. Lot of manual work will be automated.
Data Science Duniya 