標(biāo)題: Titlebook: Machine Learning for Engineers; Using data to solve Ryan G. McClarren Textbook 2021 Springer Nature Switzerland AG 2021 supervised learnin [打印本頁(yè)] 作者: 手套 時(shí)間: 2025-3-21 17:13
書(shū)目名稱Machine Learning for Engineers影響因子(影響力)
書(shū)目名稱Machine Learning for Engineers影響因子(影響力)學(xué)科排名
書(shū)目名稱Machine Learning for Engineers網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Machine Learning for Engineers網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Machine Learning for Engineers被引頻次
書(shū)目名稱Machine Learning for Engineers被引頻次學(xué)科排名
書(shū)目名稱Machine Learning for Engineers年度引用
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書(shū)目名稱Machine Learning for Engineers讀者反饋
書(shū)目名稱Machine Learning for Engineers讀者反饋學(xué)科排名
作者: Facet-Joints 時(shí)間: 2025-3-22 00:09
http://image.papertrans.cn/m/image/620619.jpg作者: 打包 時(shí)間: 2025-3-22 03:19
https://doi.org/10.1007/978-3-030-70388-2supervised learning; unsupervised learning; Bayesian statistics; linear models; tree-based models; deep n作者: NOMAD 時(shí)間: 2025-3-22 07:43
978-3-030-70390-5Springer Nature Switzerland AG 2021作者: Consequence 時(shí)間: 2025-3-22 09:09
Textbook 2021merging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines—mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates th作者: STEER 時(shí)間: 2025-3-22 16:49 作者: 職業(yè)拳擊手 時(shí)間: 2025-3-22 19:13
Recurrent Neural Networks for Time Series Dataut sequences are long. We then develop a more sophisticated network, the long short-term memory (LSTM) network to deal with longer sequences of data. Examples include predicting the frequency and shift of a signal and predicting the behavior of a cart-mounted pendulum作者: 離開(kāi)可分裂 時(shí)間: 2025-3-23 00:24 作者: 可卡 時(shí)間: 2025-3-23 04:15 作者: VERT 時(shí)間: 2025-3-23 08:55
Finding Structure Within a Data Set: Data Reduction and Clustering clusters in the data set are found using distance measures in the independent variables, and t-SNE, where high-dimensional data are mapped into a low-dimensional (2 or 3 dimensions) data set to visualize the clusters. We close this chapter by applying supervised learning methods to hyper-spectral imaging of plant leaves.作者: nettle 時(shí)間: 2025-3-23 13:20 作者: 紀(jì)念 時(shí)間: 2025-3-23 16:50 作者: 全神貫注于 時(shí)間: 2025-3-23 18:04 作者: DRILL 時(shí)間: 2025-3-24 00:59
The Landscape of Machine Learning: Supervised and Unsupervised Learning, Optimization, and Other Tope included to aid in discussions later in the text. The discussion of cross-validation includes k-fold cross-validation, leave-one-out cross-validation, and how to apply cross-validation to time series as well as problems with unknown parameters in the loss function.作者: 拱形面包 時(shí)間: 2025-3-24 04:13 作者: debris 時(shí)間: 2025-3-24 09:22 作者: 暫時(shí)中止 時(shí)間: 2025-3-24 14:21
Textbook 2021est addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow,? demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally 作者: 減弱不好 時(shí)間: 2025-3-24 18:46 作者: 排出 時(shí)間: 2025-3-24 22:05
Linear Models for Regression and Classification example considering an object in free fall to then use regression to find the acceleration due to gravity. This example then leads to a discussion of least squares regression and various generalizations using logarithmic transforms. The topic of logistic regression is presented as a classification 作者: Peculate 時(shí)間: 2025-3-24 23:19 作者: 難理解 時(shí)間: 2025-3-25 07:03 作者: Neolithic 時(shí)間: 2025-3-25 09:52 作者: LIKEN 時(shí)間: 2025-3-25 11:55
Convolutional Neural Networks for Scientific Images and Other Large Data Setsd have a truly huge number of weight and bias parameters to fit during training. For such problems rather than considering each input to be independent, we take advantage of the fact that the input has structure, even if we do not know what that structure is, by using convolutions. In a convolution 作者: 貨物 時(shí)間: 2025-3-25 19:48 作者: nephritis 時(shí)間: 2025-3-25 23:54 作者: 伸展 時(shí)間: 2025-3-26 02:31
Reinforcement Learning with Policy Gradientse do not know the correct value for the dependent variable, as we would for a supervised learning problem, but we do have an objective function called a reward. We want our machine learning model output to maximize the reward given its inputs. Additionally, the model might need to produce a series o作者: 失誤 時(shí)間: 2025-3-26 06:38
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