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9 July 2024
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N 19:24 | Loss function 6 changes history +5,213 [Mr. MacKenty (6×)] | |||
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19:24 (cur | prev) −116 Mr. MacKenty talk contribs (→Examples of Loss Functions) | ||||
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19:24 (cur | prev) +26 Mr. MacKenty talk contribs (→Examples of Loss Functions) | ||||
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19:23 (cur | prev) +11 Mr. MacKenty talk contribs (→Examples of Loss Functions) | ||||
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19:23 (cur | prev) −8 Mr. MacKenty talk contribs (→Examples of Loss Functions) | ||||
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19:23 (cur | prev) +15 Mr. MacKenty talk contribs (→Examples of Loss Functions) | ||||
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19:18 (cur | prev) +5,285 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Loss Function''' A loss function, also known as a cost function or objective function, is a mathematical function used in machine learning to measure the difference between the predicted output of a model and the actual target values. The primary goal of training a model is to minimize the loss function, thereby improving the model’s accuracy. Here’s a detailed explanation of loss functions within the context of a chatbot s...") | |||
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19:18 | Graphical processing unit (GPU) 6 changes history −21 [Mr. MacKenty (6×)] | |||
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19:18 (cur | prev) −2 Mr. MacKenty talk contribs (→Challenges in Using GPUs) | ||||
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19:18 (cur | prev) −3 Mr. MacKenty talk contribs (→Future Directions) | ||||
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19:17 (cur | prev) −3 Mr. MacKenty talk contribs (→Popular GPU Platforms for Deep Learning) | ||||
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19:17 (cur | prev) −3 Mr. MacKenty talk contribs (→Importance of GPUs in Deep Learning for Chatbots) | ||||
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19:17 (cur | prev) −3 Mr. MacKenty talk contribs (→Key Characteristics of GPUs) | ||||
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19:16 (cur | prev) −7 Mr. MacKenty talk contribs | ||||
| N 19:15 | Long short-term memory (LSTM) diffhist +5,662 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Long Short-Term Memory (LSTM)''' Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) architecture designed to handle long-term dependencies and mitigate issues such as vanishing and exploding gradients, which are common in traditional RNNs. LSTMs are particularly effective in tasks that involve sequential data, such as language modeling and time series prediction. Here’s a detailed explanation of LSTMs w...") | ||||
| N 19:13 | Latency diffhist +5,029 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Latency''' Latency refers to the time delay between a user's action and the system's response. In the context of a chatbot system, latency is a critical factor that affects user experience and the perceived responsiveness of the chatbot. Here’s a detailed explanation of latency within the context of a chatbot system: == Definition == * '''Latency''': * The time delay between the input (user query) and the output (chatbot r...") | ||||
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N 18:02 | Large language model (LLM) 3 changes history +5,182 [Mr. MacKenty (3×)] | |||
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18:02 (cur | prev) −25 Mr. MacKenty talk contribs (→Applications of LLMs in Chatbots) | ||||
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18:02 (cur | prev) −3 Mr. MacKenty talk contribs (→Key Characteristics of LLMs) | ||||
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18:02 (cur | prev) +5,210 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Large Language Model (LLM)''' A Large Language Model (LLM) is a type of artificial intelligence model that has been trained on vast amounts of text data to understand, generate, and manipulate human language. LLMs leverage advanced machine learning techniques, particularly deep learning, to perform a wide range of natural language processing (NLP) tasks. Here’s a detailed explanation of LLMs within the context of a chatbot sy...") | |||
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N 18:00 | Hyperparameter tuning 2 changes history +5,610 [Mr. MacKenty (2×)] | |||
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18:00 (cur | prev) −16 Mr. MacKenty talk contribs | ||||
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18:00 (cur | prev) +5,626 Mr. MacKenty talk contribs (Created page with "```mediawiki ''This answer was supported by a LLM'' '''Hyperparameter Tuning''' Hyperparameter tuning is the process of optimizing the hyperparameters of a machine learning model to improve its performance. Unlike model parameters, which are learned during training, hyperparameters are set before the training process begins and directly influence the behavior of the learning algorithm. Here’s a detailed explanation of hyperparameter tuning within the context of a cha...") | |||