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Machine Learning Vs Deep Learning

Basics of Machine Learning, Deep learning or Neural Networks:   Machine learning: Machine Learning is a great part of the Artificial intelligence field and is a popular way to execute AI projects through different methods. Normally in ML, there is no need to instruct computers, data is given to the system and according to prior experience and data, rules are made to apply further.  In the Machine Learning domain, models are trained on learning how to create rules for themselves to perform lookalike tasks by taking specific inputs by their desired possible outputs. ML typically requires processed or organized data with the involvement of humans. What is Deep Learning Study of neural networks is called deep learning.  DL is the study of doing ML with neural networks. In 2012, Neural Networks made up of basic and fundamental mathematical functions like addition, subtraction, multiplication and division etc. these networks  are like neurons in the human brain to make ...
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Prompt Engineering

  What is Prompt Engineering? Prompt Engineering: Prompt engineering is a way of asking a big AI system like GPT-4 to give  the answer we want. We have to be careful and clever about how to ask the question, because the AI system might not understand us or give us wrong or useless information. We can use different tricks to make our question better, like giving examples, rules, texts, or steps. Prompt engineering can help us talk to the AI system using normal language, without needing to know a lot of technical stuff. Prompt Engineering deals with the challenge of getting best possible outcomes utilizing token limits. What is the Anatomy of Prompt ? Anatomy of Prompt: In the context of language models, a prompt refers to the input or instructions provided to the model to generate the specific response or output. Characteristics of prompts are also called anatomy of prompts. Following step are needed to be taken while making a prompt; ☆Simulate persona: In this step ,first of ...
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Discriminative Artificial Intelligence Vs Generative Artificial Intelligence

  Discriminative Artificial Intelligence(AI): Discriminative Artificial Intelligence(AI) models are taught to distinguish between different data classes, patterns, images etc. In simple words it is a type of artificial intelligence that helps us tell different things apart. Discriminative AI focuses on learning the lines that separate different groups in our data. Instead of making new data, it looks at data we already have to figure out what it is.   Application Areas: Image Recognition: Discriminative AI helps identify what's in pictures, like telling if it's a cat or a dog. Speech Recognition: It figures out different words or phrases when people talk.  Natural Language Processing (NLP): It sorts text into different groups, like figuring out if a review is positive or negative. How Discriminative AI Works: Training Data: Discriminative AI learns from examples, like pictures of cats and dogs, to get better at telling them apart. Learning Boundaries: It figures ou...
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