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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...") | |||
3 July 2024
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N 09:04 | Chat bots 3 changes history +976 [Mr. MacKenty (3×)] | |||
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09:04 (cur | prev) +40 Mr. MacKenty talk contribs | ||||
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08:49 (cur | prev) +6 Mr. MacKenty talk contribs | ||||
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08:33 (cur | prev) +930 Mr. MacKenty talk contribs (Created page with "right|frame|Case study notes<ref>http://www.flaticon.com/</ref> * Backpropagation through time (BPTT) * Bag-of-words * Biases * Confirmation * Historical * Labelling * Linguistic * Sampling * Selection * Dataset * Deep learning * Graphical processing unit (GPU) * Hyperparameter tuning * Large language model (LLM) * Latency * Long short-term memory (LSTM) * Loss function * Memory...") | |||
| N 08:59 | Graphical processing unit (GPU) diffhist +5,159 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Graphical Processing Unit (GPU)''' A Graphical Processing Unit (GPU) is a specialized electronic circuit designed to accelerate the processing of images and videos. It has become essential for deep learning and other computationally intensive tasks due to its ability to perform many calculations simultaneously. Here’s a detailed explanation of GPUs within the context of a chatbot system: == Definition == * '''Graphical Proce...") | ||||
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08:57 | Deep learning 2 changes history +2,120 [Mr. MacKenty (2×)] | |||
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08:57 (cur | prev) +5,418 Mr. MacKenty talk contribs | ||||
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08:33 (cur | prev) −3,298 Mr. MacKenty talk contribs (Blanked the page) Tag: Blanking | ||||
| N 08:55 | Dataset diffhist +4,918 Mr. MacKenty talk contribs (Created page with "''This answer was supported by a LLM'' '''Dataset''' A dataset is a structured collection of data used for various purposes such as training, validating, and testing machine learning models, including chatbots. Datasets can consist of various types of data, such as text, images, audio, or numerical values, depending on the application. Here’s a detailed explanation of a dataset within the context of a chatbot system: == Definition == * '''Dataset''': * A collectio...") | ||||
| N 08:54 | Selection diffhist +4,506 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' '''Sampling Bias''' Sampling bias occurs when the sample selected for analysis is not representative of the entire population, leading to skewed or inaccurate results. This type of bias can significantly impact the performance and fairness of AI systems, including chatbots. Here’s a detailed explanation of sampling bias within the context of a chatbot system: == Definition == * '''Sampling Bias''': * A type of bias that aris...") | ||||
| N 08:53 | Sampling diffhist +4,615 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' '''Sampling''' Sampling is the process of selecting a subset of data from a larger dataset to train machine learning models, including chatbots. The goal of sampling is to create a representative subset that accurately reflects the characteristics of the entire dataset. Here’s a detailed explanation of sampling within the context of a chatbot system: == Definition == * '''Sampling''': * The technique of choosing a smaller, m...") | ||||
| N 08:52 | Linguistic diffhist +4,314 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' '''Linguistic Bias''' Linguistic bias refers to prejudice or favoritism toward certain languages, dialects, or ways of speaking within AI systems. This type of bias can impact the performance and fairness of chatbots and other language-based technologies. Here’s a detailed explanation of linguistic bias within the context of a chatbot system: == Definition == * '''Linguistic Bias''': * Bias that occurs when AI systems favor...") | ||||
| N 08:51 | Labelling diffhist +4,208 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' '''Labelling Bias''' Labelling bias occurs when the labels assigned to training data reflect subjective judgments, stereotypes, or prejudices, rather than objective truth. This type of bias can significantly impact the performance and fairness of AI systems, including chatbots. Here’s a detailed explanation of labelling bias within the context of a chatbot system: == Definition == * '''Labelling Bias''': * Bias introduced du...") | ||||
| N 08:49 | Historical diffhist +4,070 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' Historical bias refers to distortions or inaccuracies in data that arise from historical inequalities, prejudices, or systemic discrimination embedded within the data. This type of bias can significantly affect the training and performance of AI systems, including chatbots. Here’s a detailed explanation of historical bias within the context of a chatbot system: == Definition == * '''Historical Bias''': * Bias in data that re...") | ||||
| N 08:48 | Confirmation diffhist +3,758 Mr. MacKenty talk contribs (Created page with " ''This answer was supported by a LLM'' Confirmation, often referred to as confirmation bias, is the tendency to search for, interpret, and remember information in a way that confirms one’s preexisting beliefs or hypotheses. This cognitive bias can significantly affect decision-making processes and the development of AI systems. Here’s a detailed explanation of confirmation within the context of a chatbot system: == Definition == * '''Confirmation Bias''': * Th...") | ||||
| N 08:44 | Biases diffhist +4,575 Mr. MacKenty talk contribs (Created page with "This article was created with the help and support of an LLM Bias refers to a systematic error or deviation from true values or fairness that affects data, models, and decision-making processes. In the context of machine learning and artificial intelligence, bias can lead to skewed results and unfair treatment of certain groups. Here’s a detailed explanation of bias within the context of a chatbot system: == Types of Bias == * '''Data Bias''': * Occurs when the tr...") | ||||
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N 08:40 | Bag-of-words 2 changes history +3,657 [Mr. MacKenty (2×)] | |||
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08:40 (cur | prev) −17 Mr. MacKenty talk contribs | ||||
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08:40 (cur | prev) +3,674 Mr. MacKenty talk contribs (Created page with "This wiki article was made with the help and support of an LLM ```mediawiki '''Bag of Words (BoW)''' Bag of Words (BoW) is a fundamental technique used in natural language processing (NLP) and information retrieval to represent text data in a structured format. It simplifies the text data by treating it as a collection of individual words, disregarding grammar and word order, but keeping track of word frequency. Here’s a detailed explanation of BoW within the context...") | |||
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N 08:38 | Backpropagation through time (BPTT) 2 changes history +3,480 [Mr. MacKenty (2×)] | |||
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08:38 (cur | prev) +69 Mr. MacKenty talk contribs | ||||
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08:38 (cur | prev) +3,411 Mr. MacKenty talk contribs (Created page with "Backpropagation Through Time (BPTT) is an extension of the backpropagation algorithm for training recurrent neural networks (RNNs). RNNs are designed to handle sequential data by maintaining a state that can capture information from previous inputs. However, training RNNs is challenging due to their complex structure and the need to account for dependencies over time. Here’s a detailed explanation of BPTT within the context of a chatbot system: 1. Sequential Nature o...") | |||
| 08:34 | Natural language processing diffhist −1,521 Mr. MacKenty talk contribs (Replaced content with " == Standards == * Discuss the latest advances in natural language processing. == References == <references /> Category:Modeling and Simulation") | ||||
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N 08:31 | 2025 case study 4 changes history +1,388 [Mr. MacKenty (4×)] | |||
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08:31 (cur | prev) +1 Mr. MacKenty talk contribs (→Every term in the case study) | ||||
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08:29 (cur | prev) +1 Mr. MacKenty talk contribs (→The case study) | ||||
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08:28 (cur | prev) 0 Mr. MacKenty talk contribs | ||||
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08:28 (cur | prev) +1,386 Mr. MacKenty talk contribs (Created page with "right|frame|Case study<ref>http://www.flaticon.com/</ref> == Introduction == Higher-level students must write 3 papers. The case study is the third paper. Every year the case study discusses a different topic. Students must become '''very familiar''' with the case study. The IB recommends spending about a year studying this case study. This page will help you organize and understand the 2024 case study. == The case study == Media:D 4 c...") | |||
| 08:27 | Welcome diffhist 0 Mr. MacKenty talk contribs | ||||
26 June 2024
| 15:01 | Getting started diffhist +47 Mr. MacKenty talk contribs (→Bookmark the following sites) | ||||
| 10:20 | MediaWiki:Sidebar diffhist +33 Mr. MacKenty talk contribs | ||||