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Understanding machine learning for data analytics in simple terms

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Photo source: pexels.com In this article, we'll explore the core concept to machine learning and elaborate the essential features and implementation of machine learning. As the data continues to grow along with the subsequent analytical requirement, depending on the human brain to cater to the growing requirement and synthesize the data, becomes all the more difficult and inconvenient. Now, machine learning is an algorithmic approach of making machines intelligent by learning from the data and derived training models by utilizing the data and generalized decisions. Machine learning is a branch of artificial intelligence which utilizes intuitive algorithms to minimize human intervention and build smart systems capable of deriving decision and solutions.  Machine learning is proven effective when working with datasets that are large, diversified, and rapidly changing, where managing such datasets with human interventions can be tedious. Machine Learning Technologies.  Here, we n...
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The basic difference between machine learning and deep learning

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Photo source: pexels.com In this article, we'll explore the critical features of machine learning. Along with the comparable features of machine learning and deep learning. We can utilize the capabilities afforded by machine learning to resolve various categories of problems. Now, machine learning utilizes various approaches in order to resolve and provide solutions. Some of the approaches adopted by machine learning to resolve problems includes Classification, Clustering, Forecasting, Simulation, Regression, and Optimization. With the Classification approach, the application learns from the input data. And then utilizes what it has learned to classify newly identified observations. We can utilize Clustering algorithms to classify each data point into a specific group, depending on the similarity of the identified attributes. Now the objective of the forecasting algorithms provided in machine learning is to achieve accuracy of data against time. Simulation provides a view of simula...
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Introduction to the fine tuning in Large Language Models

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Fine tuning in LLM In this article, we're going to delve into the techniques used to adapt a generic pre-trained model for a specific task or application. Now, pre-trained language models can do really impressive stuff just immediately off the shelf, including things like text generation, summarization, and even providing levels of code. But LLMs are not one-size-fits-all as you can imagine and maybe I want to specialize it even further after the model has been trained. I'd like to make it really good at answering questions based on certain topics such as medicine or travel or code or something. This is what the idea of fine-tuning comes in.  Methods used in Fine tuning: Fine-tuning a large language model refers to the process of taking a pre-trained language model or a model that's already been trained on a large dataset and further training it on a smaller task-specific dataset. Now this process will allow the model to adapt and specialize its capability. And if we take t...
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