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Fine-Tune Large Language Models(LLMs)

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Natural language processing has undergone a transformation thanks to large language models (LLMs), which exhibit remarkable abilities and innovative solutions. These models, trained on vast amounts of textual data, perform exceptionally well in generating text, translating languages, summarizing content, and answering questions. However, despite their strengths, LLMs may not always be perfectly suited for specialized tasks or specific domains. In this article, we will delve into how fine-tuning large language models (LLMs) can greatly enhance their performance, lower training costs, and provide more precise and context-specific outcomes. When discussing fine-tuning, it's crucial to understand the parameters of the model. A model is essentially a network composed of weights and biases, which are the parameters. In any neural network, there are inputs, outputs, and hidden layers. Each node within these layers has associated weights and biases, which collectively define the parameters...
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Retrieval Augmented Generation(RAG)

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 We Know core ideas like NLP, LLM. Let's look at how we might build utilizing LLM and the deployment aspect using RAG. Retrieval-Augmented Generation (RAG) is a technique in natural language processing that combines retrieval-based and generation-based approaches to create more accurate and informative responses. Let's break down each point: 1. RAG Architecture  Architecture: Retriever: Retrieves relevant documents or passages from a large corpus based on the input query. Reader/Generator: Uses the retrieved documents to generate a coherent and relevant response. Combiner: Integrates the retrieved information with the generated response. 2. Pipeline Behind the RAG The RAG pipeline is a collection of the above components working together: Query Processing: The input query is encoded into a vector. Retrieval: The encoded query is used to retrieve relevant documents from a vector database. Document Encoding: The retrieved documents are encoded into vectors. Information Inte...
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Large Language Models(LLMs)

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  Language Model A language model is a type of artificial intelligence model designed to understand, generate, and manipulate human language. Language models are trained on large datasets of text to learn patterns in language use, including grammar, vocabulary, and context. Key functions include: Text Generation : Producing coherent and contextually relevant text. Text Completion : Predicting the next word or phrase in a given context. Machine Translation : Translating text from one language to another. Sentiment Analysis : Determining the sentiment expressed in a piece of text. Text Summarization : Creating concise summaries of longer texts. Language models can be based on various architectures, such as Recurrent Neural Networks (RNNs) and Transformer models. Large Language Model (LLM) A large language model (LLM) is a type of language model that is characterized by its large size, typically measured by the number of parameters (weights) it has. LLMs are trained on vast amounts ...
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Transformers: Self-attention

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 In the realm of neural networks, three primary types are commonly discussed: Artificial Neural Networks (ANNs) : These are fully connected networks comprising input, hidden, and output layers. Each neuron in a layer is connected to every neuron in the subsequent layer, enabling complex pattern recognition through weighted connections. Convolutional Neural Networks (CNNs) : CNNs incorporate convolutional layers with kernels (filters) that slide across the input data to detect features. These networks also perform pooling operations to reduce dimensionality and flatten the data before passing it to fully connected layers. CNNs are particularly effective for image and spatial data processing. Recurrent Neural Networks (RNNs) : RNNs are designed to handle sequential data by maintaining a memory of previous inputs through their hidden states. Unlike feed-forward networks, RNNs can process input sequences of variable length, making them suitable for tasks involving time series data, nat...
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Hypothesis Testing - Statistics

  1. What is Hypothesis Testing and when do we use it? Hypothesis testing is a part of statistical analysis, where we test the assumptions made regarding a population parameter. It is generally used when we were to compare: a single group with an external standard two or more groups with each other A  Parameter  is a number that describes the data from the  population  whereas, a  Statistic  is a number that describes the data from a  sample . 2. Terminology used Null Hypothesis:  Null hypothesis is a statistical theory that suggests there is no statistical significance exists between the populations. Alternative Hypothesis:  An Alternative hypothesis suggests there is a significant difference between the population parameters. It could be greater or smaller. Basically, it is the contrast of the Null Hypothesis. Note: H 0  must always contain equality(=). H a  always contains difference( ≠,  >, <). For example, if we...
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