標(biāo)題: Titlebook: Explainable AI Recipes; Implement Solutions Pradeepta Mishra Book 2023 Pradeepta Mishra 2023 Explainable AI.Python.Artificial Intelligence [打印本頁(yè)] 作者: 無(wú)法仿效 時(shí)間: 2025-3-21 16:07
書(shū)目名稱Explainable AI Recipes影響因子(影響力)
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書(shū)目名稱Explainable AI Recipes網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Explainable AI Recipes網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Explainable AI Recipes被引頻次
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書(shū)目名稱Explainable AI Recipes讀者反饋
書(shū)目名稱Explainable AI Recipes讀者反饋學(xué)科排名
作者: seduce 時(shí)間: 2025-3-21 21:20
Explainability for Deep Learning Models,uch as audio processing, text classification, etc.; deep neural networks, which are used for building extremely deep networks; and finally, convolutional neural network models, which are used for image classification.作者: 微粒 時(shí)間: 2025-3-22 01:58 作者: Lacerate 時(shí)間: 2025-3-22 06:11 作者: 安撫 時(shí)間: 2025-3-22 12:22
https://doi.org/10.1007/978-3-030-19175-7 number of features for a machine learning task increases or the volume of data increases, then it takes a lot of time to apply machine learning techniques. That’s when deep learning techniques are used.作者: CUMB 時(shí)間: 2025-3-22 13:13
Introducing Explainability and Setting Up Your Development Environment, number of features for a machine learning task increases or the volume of data increases, then it takes a lot of time to apply machine learning techniques. That’s when deep learning techniques are used.作者: CUMB 時(shí)間: 2025-3-22 17:06 作者: 吹牛大王 時(shí)間: 2025-3-22 21:55
Mirjana Pavlovic,John Mayfield,Bela Balint business to plan better and will help decision-makers to plan according to the future estimations. There are machine learning–based techniques that can be applied to generate future forecasting; also, there is a need to explain the predictions about the future.作者: 高射炮 時(shí)間: 2025-3-23 03:32 作者: fledged 時(shí)間: 2025-3-23 05:53 作者: 廢墟 時(shí)間: 2025-3-23 13:17
Handbook of Mathematical Geodesy the case of multinomial output variables, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model.作者: cuticle 時(shí)間: 2025-3-23 14:01 作者: 痛苦一下 時(shí)間: 2025-3-23 18:20
Kathrin Natterer (née Greuling)ple models are being trained, and each model generates a classification. The final model takes into account the majority voting rule criteria to decide the final prediction. Because of the nature of ensemble models, these are harder to explain to end users. That is why we need frameworks that can explain the ensemble models.作者: wall-stress 時(shí)間: 2025-3-24 01:06
Ethical Issues in Media Psychology,rain a machine learning model to perform text classification such as customer review classification, feedback classification, newsgroup classification, etc. In this chapter, we will be using explainable libraries to explain the predictions or classifications.作者: Trigger-Point 時(shí)間: 2025-3-24 03:30
Explainability for Linear Supervised Models, the case of multinomial output variables, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model.作者: Prognosis 時(shí)間: 2025-3-24 08:39 作者: extract 時(shí)間: 2025-3-24 11:56 作者: 休戰(zhàn) 時(shí)間: 2025-3-24 17:32 作者: 四指套 時(shí)間: 2025-3-24 22:39 作者: Occipital-Lobe 時(shí)間: 2025-3-25 00:37
http://image.papertrans.cn/e/image/319278.jpg作者: 貪婪地吃 時(shí)間: 2025-3-25 05:43
https://doi.org/10.1007/978-3-030-19175-7al trials, and drug testing. There are regulatory requirements in some of these industries where model explainability is required. Artificial intelligence involves classifying objects, recognizing the objects to detect fraud, and so forth. Every learning system requires three things: input data, pro作者: Bravura 時(shí)間: 2025-3-25 08:22 作者: Vulnerable 時(shí)間: 2025-3-25 14:52
Variational Segmentation with Shape Priors in the case of classification, the output variable is binary or multinomial. A binary output variable has two outcomes, such as true and false, accept and reject, yes and no, etc. In the case of a multinomial output variable, the outcome can be more than two, such as high, medium, and low. In this 作者: 不斷的變動(dòng) 時(shí)間: 2025-3-25 16:17
Kathrin Natterer (née Greuling)ions are aggregated in ensemble models to generate the final models. In the case of supervised regression models, many models are generated, and the averages of all the predictions are taken into consideration to generate the final prediction. Similarly, for supervised classification problems, multi作者: 分期付款 時(shí)間: 2025-3-25 23:37 作者: hegemony 時(shí)間: 2025-3-26 01:06 作者: kindred 時(shí)間: 2025-3-26 04:45 作者: commodity 時(shí)間: 2025-3-26 12:16 作者: Hyperplasia 時(shí)間: 2025-3-26 12:40 作者: 感染 時(shí)間: 2025-3-26 18:32 作者: 和平主義 時(shí)間: 2025-3-26 21:04 作者: intercede 時(shí)間: 2025-3-27 03:29
Explainability for Nonlinear Supervised Models, in the case of classification, the output variable is binary or multinomial. A binary output variable has two outcomes, such as true and false, accept and reject, yes and no, etc. In the case of a multinomial output variable, the outcome can be more than two, such as high, medium, and low. In this 作者: notion 時(shí)間: 2025-3-27 07:11
Explainability for Ensemble Supervised Models,ions are aggregated in ensemble models to generate the final models. In the case of supervised regression models, many models are generated, and the averages of all the predictions are taken into consideration to generate the final prediction. Similarly, for supervised classification problems, multi作者: Between 時(shí)間: 2025-3-27 10:02
Explainability for Natural Language Processing,ELI5. The objective of explaining the text classification tasks or sentiment analysis tasks is to let the user know how a decision was made. The predictions are generated using a supervised learning model for unstructured text data. The input is a text sentence or many sentences or phrases, and we t作者: deficiency 時(shí)間: 2025-3-27 16:46 作者: 半身雕像 時(shí)間: 2025-3-27 21:33 作者: 預(yù)定 時(shí)間: 2025-3-28 00:45 作者: Terrace 時(shí)間: 2025-3-28 03:08 作者: Supplement 時(shí)間: 2025-3-28 09:21
