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Pseudo-randomness refers to sequences of numbers that appear random but are generated by a deterministic process, typically through an algorithm. These sequences are crucial for simulations, cryptography, and computational applications where random-like behavior is needed without true randomness.
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Self-attention is a mechanism in neural networks that allows the model to weigh the importance of different words in a sentence relative to each other, enabling it to capture long-range dependencies and contextual relationships. It forms the backbone of Transformer architectures, which have revolutionized natural language processing tasks by allowing for efficient parallelization and improved performance over sequential models.
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Multi-Head Attention is a mechanism that allows a model to focus on different parts of an input sequence simultaneously, enhancing its ability to capture diverse contextual relationships. By employing multiple attention heads, it enables the model to learn multiple representations of the input data, improving performance in tasks like translation and language modeling.
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The Encoder-Decoder structure is a neural network architecture primarily used for sequence-to-sequence tasks, where the encoder processes the input sequence into a context vector and the decoder generates the output sequence from this vector. This architecture is fundamental in applications like machine translation, where it allows for flexible handling of variable-length input and output sequences.
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Positional encoding is a technique used in transformer models to inject information about the order of input tokens, which is crucial since transformers lack inherent sequence awareness. By adding or concatenating Positional encodings to input embeddings, models can effectively capture sequence information without relying on recurrent or convolutional structures.
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Layer Normalization is a technique used to stabilize and accelerate the training of deep neural networks by normalizing the inputs across the features for each training case. It differs from Batch Normalization by normalizing the inputs within a single layer, making it more suitable for recurrent neural networks and situations where batch sizes are small or vary.
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Feedforward Neural Networks are a type of artificial neural network where connections between the nodes do not form a cycle, meaning data moves in one direction from input to output. They are the simplest form of neural networks and are primarily used for pattern recognition and classification tasks.
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Attention mechanisms are a crucial component in neural networks that allow models to dynamically focus on different parts of the input data, enhancing performance in tasks like machine translation and image processing. By assigning varying levels of importance to different input elements, Attention mechanisms enable models to handle long-range dependencies and improve interpretability.
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Scalability refers to the ability of a system, network, or process to handle a growing amount of work or its potential to accommodate growth. It is a critical factor in ensuring that systems can adapt to increased demands without compromising performance or efficiency.
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Parallelization is the process of dividing a computational task into smaller, independent tasks that can be executed simultaneously across multiple processors or cores, thereby reducing the overall execution time. It is a fundamental technique in high-performance computing and is essential for efficiently utilizing modern multi-core and distributed computing architectures.
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Natural language processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics, focused on enabling computers to understand, interpret, and generate human language. It encompasses a wide range of applications, from speech recognition and sentiment analysis to machine translation and conversational agents, leveraging techniques like machine learning and deep learning to improve accuracy and efficiency.
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GPT, or Generative Pre-trained Transformer, is an advanced language model developed by OpenAI that uses deep learning to produce human-like text. It leverages a transformer architecture to predict the next word in a sentence, enabling it to generate coherent and contextually relevant responses across a wide range of topics.
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Pre-trained language models are neural network models trained on large corpora of text data to understand and generate human language, allowing them to be fine-tuned for specific tasks such as translation, summarization, and sentiment analysis. These models leverage transfer learning to improve performance and reduce the amount of labeled data needed for downstream tasks.
Learned positional embeddings are a technique used in transformer models to provide information about the position of tokens in a sequence, allowing the model to capture the order of words. Unlike fixed positional encodings, learned embeddings are trainable parameters that can adapt to the specific data and task, potentially improving model performance.
GPT is a state-of-the-art language model that uses deep learning to generate human-like text based on the input it receives. It leverages a transformer architecture and is pre-trained on vast amounts of text data, allowing it to perform a wide range of natural language processing tasks with minimal fine-tuning.
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A context window in natural language processing refers to the span of text that a model considers when making predictions or generating responses. The size of the context window can significantly impact the model's performance, affecting both its ability to maintain coherence and its computational efficiency.
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The Query-Key-Value model is a foundational mechanism in attention mechanisms, particularly in transformer architectures, enabling the model to focus on different parts of the input data dynamically. It works by computing a weighted sum of the values, where the weights are determined by a compatibility function between the query and the keys, allowing for efficient handling of long-range dependencies in sequences.
Generative Pre-trained Transformers (GPT) are a class of language models that leverage unsupervised learning on large text corpora to generate coherent and contextually relevant text. They utilize a transformer architecture to capture long-range dependencies and fine-tune on specific tasks to enhance performance in natural language understanding and generation.
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Masked Language Models (MLMs) are a type of neural network architecture used in natural language processing where parts of the input text are masked or hidden, and the model learns to predict these masked tokens based on their context. This approach enables the model to gain a deep understanding of language semantics and syntactic structures, making it effective for tasks like text completion, translation, and sentiment analysis.
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Bidirectional context refers to the ability of a model to consider both preceding and succeeding information in a sequence to understand and generate language more accurately. This approach enhances the model's comprehension and prediction capabilities by leveraging context from both directions, unlike unidirectional models that only process sequences in one direction.
The Text-to-Text Transfer Transformer (T5) is a unified framework for natural language processing tasks that treats every problem as a text-to-text problem, allowing for a single model to be fine-tuned across diverse tasks. This approach leverages transfer learning to achieve state-of-the-art results by pre-training on a large dataset and fine-tuning on specific tasks.
Bidirectional Encoder Representations from Transformers (BERT) is a revolutionary natural language processing model developed by Google that uses deep learning to understand the context of words in a sentence by looking at both preceding and succeeding words. This bidirectional approach enables BERT to achieve state-of-the-art results in various NLP tasks such as question answering and sentiment analysis.
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Machine Learning in NLP involves using algorithms and models to enable computers to understand, interpret, and generate human language. It leverages techniques like neural networks and deep learning to process and analyze vast amounts of textual data, improving tasks such as translation, sentiment analysis, and information retrieval.
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Masked Language Modeling (MLM) is a self-supervised learning technique used in natural language processing where certain words in a sentence are masked and the model is trained to predict these masked words based on the surrounding context. This approach enables the model to learn bidirectional representations of text, significantly improving its understanding and generation capabilities.
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Next Sentence Prediction (NSP) is a task used in training language models to understand the relationship between two sentences by predicting whether a given sentence logically follows another. It is a crucial component in pre-training models like BERT, enhancing their ability to grasp context and improve performance on downstream tasks such as question answering and sentiment analysis.
Bidirectional Encoder Representations (BERT) is a deep learning model that revolutionizes natural language processing by understanding the context of a word based on its surrounding words in a sentence, using a transformer-based architecture. It achieves state-of-the-art performance by pre-training on a large corpus of text and fine-tuning on specific tasks such as question answering and sentiment analysis.
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