標題: Titlebook: Machine Learning with Python; Theory and Implement Amin Zollanvari Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exc [打印本頁] 作者: 面臨 時間: 2025-3-21 16:34
書目名稱Machine Learning with Python影響因子(影響力)
書目名稱Machine Learning with Python影響因子(影響力)學科排名
書目名稱Machine Learning with Python網絡公開度
書目名稱Machine Learning with Python網絡公開度學科排名
書目名稱Machine Learning with Python被引頻次
書目名稱Machine Learning with Python被引頻次學科排名
書目名稱Machine Learning with Python年度引用
書目名稱Machine Learning with Python年度引用學科排名
書目名稱Machine Learning with Python讀者反饋
書目名稱Machine Learning with Python讀者反饋學科排名
作者: obscurity 時間: 2025-3-21 23:41 作者: 使?jié)M足 時間: 2025-3-22 03:13
https://doi.org/10.1007/978-3-031-33342-2Keras-TensorFlow; Clustering; Convolutional Neural Networks; Decision Trees; Deep Learning; Ensemble Lear作者: Ascendancy 時間: 2025-3-22 05:31 作者: 謙卑 時間: 2025-3-22 10:38 作者: NIP 時間: 2025-3-22 16:52
k-Nearest Neighbors, kNN (short for k-Nearest Neighbors) not only because of its simplicity, but also due to its long history in machine learning. As a result, in this chapter we formalize the kNN mechanism for both classification and regression and we will see various forms of kNN.作者: CON 時間: 2025-3-22 19:49
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o作者: 優(yōu)雅 時間: 2025-3-22 23:55
Amin Zollanvariented of experimental Raman scattering studies and their interpretation based on models of the lattice dynamics of pristine and intercalated graphite. The periodic layer structure of intercalation compounds makes it possible to model the dynamical matrix by a Brillouin zone folding of the pristine g作者: 壕溝 時間: 2025-3-23 05:26
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o作者: eulogize 時間: 2025-3-23 08:30 作者: Fsh238 時間: 2025-3-23 10:57
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o作者: Constrain 時間: 2025-3-23 16:38 作者: scoliosis 時間: 2025-3-23 18:20
Amin Zollanvarie excitations, the excitonic polaritons. It helps to obtain evidence for the predictions made by Hopfield and others of some extraordinary properties of these objects. Let us mention, for instance, the dispersive, multiple Brillouin peaks, the AS/S ratio in RBS larger than unity, the disappearence o作者: ARIA 時間: 2025-3-23 23:46 作者: 一大群 時間: 2025-3-24 02:42
Getting Started with Python,iven such an important role of Python for machine learning, this chapter provides a quick introduction to the language. The introduction is mainly geared toward those who have no knowledge of Python programming but are familiar with programming principles in another (object oriented) language; for e作者: Antigen 時間: 2025-3-24 09:41
Three Fundamental Python Packages,ious Python data science libraries including Scikit-Learn, which will be extensively used in Chapters 4-12. Another useful library for data analysis is pandas, which is built on top of NumPy. This package particularly facilitates working with tabular data. Last but not least is the matplotlib, which作者: Entropion 時間: 2025-3-24 13:13 作者: 討好女人 時間: 2025-3-24 16:44 作者: Musculoskeletal 時間: 2025-3-24 22:42
Linear Models,halogram (EEG) classification, speech recognition, face recognition, to just name a few. Their popularity in various applications is mainly attributed to one or a combination of the following factors: 1) their simple structure and training efficiency; 2) interpretability; 3) matching their complexit作者: Exonerate 時間: 2025-3-25 03:12 作者: NUL 時間: 2025-3-25 04:30 作者: cuticle 時間: 2025-3-25 10:40 作者: PATHY 時間: 2025-3-25 14:04
Feature Selection,ensionality reduction is . in which a set of candidate features are transformed to a lower cardinality set of new features using some linear or non-linear mathematical transformations. Feature selection can be seen as a special type of feature extraction in which the physical nature of features are 作者: 使害羞 時間: 2025-3-25 16:28
Assembling Various Learning Steps,alization, imputation, feature selection/extraction, model selection and training, and evaluation. The misuse of some of these steps in connection with others have been so common in various fields of study that have occasionally triggered warnings and criticism from the machine learning community. T作者: BLOT 時間: 2025-3-25 20:49
Clustering, “classification” that was covered extensively in previous chapters, clustering is based on the assumption that there is no information about class labels to guide the process of grouping. In other words, clustering is an unsupervised classification of observations. Broadly speaking, “exclusive” (al作者: Cursory 時間: 2025-3-26 01:40
Deep Learning with Keras-TensorFlow,ment learning. Among all family of predictive models that are used in machine learning, by deep learning we exclusively refer to a particular class of models known as multilayered Artificial Neural Network (ANN), which are partially inspired by our understanding of biological neural circuits. Genera作者: 兵團 時間: 2025-3-26 07:42 作者: expire 時間: 2025-3-26 12:24 作者: 新手 時間: 2025-3-26 14:58 作者: Incompetent 時間: 2025-3-26 20:51
ory of Machine Learning and implementation in Python.SupplemThis book is meant as a textbook for undergraduate and graduate students who are willing to understand essential elements of machine learning from both a theoretical and a practical perspective. The choice of the topics in the book is made 作者: Instantaneous 時間: 2025-3-26 23:49
Three Fundamental Python Packages,s pandas, which is built on top of NumPy. This package particularly facilitates working with tabular data. Last but not least is the matplotlib, which is the primary Python plotting library built on NumPy. When it comes to Python codes, NumPy package is known as numpy but in what follows, we use NumPy and numpy interchangeably.作者: HAIRY 時間: 2025-3-27 02:57 作者: FLIP 時間: 2025-3-27 08:03 作者: Aggressive 時間: 2025-3-27 10:11 作者: Evacuate 時間: 2025-3-27 14:07 作者: 相符 時間: 2025-3-27 18:43 作者: 進入 時間: 2025-3-28 01:29
Amin Zollanvariraction. It provides a huge amount of information on exciton parameters [masses, L-T splittings (i.e., polarizability), fine structure parameters (exchange energy)], and semiconductor parameters (refractive indices, sound velocities, phonon energies, .-linear energy terms, impurity energy levels and作者: 恃強凌弱 時間: 2025-3-28 04:09
Amin Zollanvarisharp vibrational mode and a Ramanactive continuum. Second-order Raman scattering results for intercalated graphite are reported. A brief summary is also given on Raman scattering studies of intercalated graphite fibers, adsorbed molecules on graphite surfaces and ion-implanted graphite.作者: ferment 時間: 2025-3-28 09:05 作者: 尖叫 時間: 2025-3-28 14:13 作者: FELON 時間: 2025-3-28 17:28 作者: floaters 時間: 2025-3-28 18:47 作者: WATER 時間: 2025-3-29 00:17 作者: Saline 時間: 2025-3-29 03:03 作者: Biomarker 時間: 2025-3-29 11:15
Ensemble Learning,element is induced in the splitting strategy. This randomization often leads to improvement over bagged trees. In pasting, we randomly pick modest-size subsets of a large training data, train a predictive model on each, and aggregate the predictions. In boosting a sequence of weak models are trained作者: 庇護 時間: 2025-3-29 13:29 作者: Introvert 時間: 2025-3-29 19:27
Assembling Various Learning Steps, with resampling evaluation rules. To keep discussion succinct, we use feature selection and cross-validation as typical representatives of the composite process and a resampling evaluation rule, respectively. We then describe appropriate implementation of作者: 散開 時間: 2025-3-29 20:35
Deep Learning with Keras-TensorFlow,n this regard, we use multi-layer perceptrons as a typical ANN and postpone other architectures to later chapters. In terms of software, we switch to Keras with TensorFlow backend as they are welloptimized for training and tuning various forms of ANN and support various forms of hardware including C作者: affinity 時間: 2025-3-30 02:50 作者: originality 時間: 2025-3-30 05:49
Recurrent Neural Networks,its input observations and weights. Therefore, in contrast with other common architectures used in deep learning, RNN is capable of learning sequential dependencies extended over time. As a result, it has been extensively used for applications involving analyzing sequential data such as time-series,作者: meditation 時間: 2025-3-30 08:57
Textbook 2023an introduction to fundamental Python packages for data science and machine learning such as NumPy, Pandas, Matplotlib, Scikit-Learn, XGBoost, and Keras with TensorFlow backend.?.Given the current dominant role of the Python programming language for machine learning, the book complements the theoret作者: 騷擾 時間: 2025-3-30 13:51 作者: 改變立場 時間: 2025-3-30 19:54
10樓作者: 才能 時間: 2025-3-30 23:39
10樓
