標題: Titlebook: Machine Learning for Computer Scientists and Data Analysts; From an Applied Pers Setareh Rafatirad,Houman Homayoun,Sai Manoj Puduko Textboo [打印本頁] 作者: Optician 時間: 2025-3-21 18:05
書目名稱Machine Learning for Computer Scientists and Data Analysts影響因子(影響力)
書目名稱Machine Learning for Computer Scientists and Data Analysts影響因子(影響力)學科排名
書目名稱Machine Learning for Computer Scientists and Data Analysts網(wǎng)絡公開度
書目名稱Machine Learning for Computer Scientists and Data Analysts網(wǎng)絡公開度學科排名
書目名稱Machine Learning for Computer Scientists and Data Analysts被引頻次
書目名稱Machine Learning for Computer Scientists and Data Analysts被引頻次學科排名
書目名稱Machine Learning for Computer Scientists and Data Analysts年度引用
書目名稱Machine Learning for Computer Scientists and Data Analysts年度引用學科排名
書目名稱Machine Learning for Computer Scientists and Data Analysts讀者反饋
書目名稱Machine Learning for Computer Scientists and Data Analysts讀者反饋學科排名
作者: 匯總 時間: 2025-3-22 00:11
A Brief Review of Probability Theory and Linear Algebrarete examples. Numerous subjects are explored, including conditional probability, discrete random variables, and continuous random variables. Additionally, the chapter discusses common discrete and continuous probability distributions. The topic of learning matrix decomposition rules applicable to m作者: charisma 時間: 2025-3-22 01:28
Supervised Learningis the process of acquiring knowledge about annotated data and deriving relationships between the input data and the labels. The simplicity and capacity to develop a better model are two of the key advantages of supervised learning over other types of learning (unsupervised and reinforcement learnin作者: 燒瓶 時間: 2025-3-22 05:35
Unsupervised Learningls (target), and the machine learning model is supposed to fit the learning curve to the dataset and labels. However, there are numerous situations in which the data may not be labeled. Algorithms are necessary for these circumstances to discover relevant patterns, structures, or groupings within th作者: acrobat 時間: 2025-3-22 10:36
Reinforcement Learninghere exist other scenarios where the data is labeled partially and critical to learning from the experience of the system. For such scenarios, reinforcement learning can be utilized. Reinforcement learning is a kind of machine learning technique that mimics one of the most common learning styles in 作者: Foolproof 時間: 2025-3-22 15:57
Online Learningt to handle. Online learning approaches strive to update the best predictor for the data in a sequential sequence, as a typical strategy used in areas of machine learning to tackle the computational infeasibility of training throughout the full dataset. In this chapter, we will go through the two ma作者: 整理 時間: 2025-3-22 18:13 作者: NAIVE 時間: 2025-3-23 00:28
Graph Learningd. Applying typical deep learning techniques (such as convolutional neural networks) to this non-Euclidean structure is not straightforward. As a result, graph neural networks (GNNs) are proposed as a way to combine node attributes with graph topology, thereby establishing themselves as a widely rec作者: Host142 時間: 2025-3-23 01:34
Adversarial Machine Learningments in the machine learning, the vulnerabilities in those techniques are as well exploited. Adversarial samples are the samples generated by adding crafted perturbations to the normal input samples. An overview of different techniques to generate adversarial samples, defense to make classifiers ro作者: fibula 時間: 2025-3-23 06:06
SensorNet: An Educational Neural Network Framework for Low-Power Multimodal Data Classificatione-series signals. Time-series signals generated by different sensor modalities with different sampling rates are first converted into images (2-D signals), and then DCNN is utilized to automatically learn shared features in the images and perform the classification. SensorNet: (1) is scalable as it 作者: Licentious 時間: 2025-3-23 11:44 作者: Immunoglobulin 時間: 2025-3-23 16:09
Applied Machine Learning for Computer Architecture Securityr increasingly sophisticated cyber-attacks. Cybersecurity for the past decades has been at the forefront of global attention as a critical threat to the society, especially the nation’s information technology infrastructure. Attackers are increasingly motivated and enabled to compromise software and作者: 多嘴 時間: 2025-3-23 21:53 作者: 消散 時間: 2025-3-23 23:24
A Brief Review of Probability Theory and Linear Algebraally, the chapter discusses common discrete and continuous probability distributions. The topic of learning matrix decomposition rules applicable to machine learning algorithms is examined, as well as their applicability.作者: adjacent 時間: 2025-3-24 05:57
Recommender Learningtorical actions. User actions on items can be categorized into two classes: (1) . and (2) .. Explicit feedbacks refer to user ratings or likes on items where the user’s preferences are explicitly expressed. Implicit feedbacks include users’ clicks, browses, and stay time on items.作者: Perceive 時間: 2025-3-24 07:35 作者: 反感 時間: 2025-3-24 13:43 作者: Ataxia 時間: 2025-3-24 16:48 作者: Mucosa 時間: 2025-3-24 20:04
Online Learningods are also used in a variety of unsupervised applications, including clustering and dimension reduction. Finally, some real-world examples of online learning applications are shown to demonstrate the value of online learning in the real world.作者: Calculus 時間: 2025-3-25 01:50
Graph Learningthe principles of GNN mathematics. Then, within a unified framework, multiple types of GNNs are introduced in the spectral and spatial domains, respectively, with the goal of improving performance. Finally, this chapter covers GNN applications and gives links to the literature on benchmarks and implementations.作者: 額外的事 時間: 2025-3-25 05:04
Adversarial Machine Learningints are experimentally provided, such as up to 97.65% accuracy even against CW attack. Though adversarial learning’s effectiveness is enhanced, still it is shown in this work that it can be further exploited for vulnerabilities.作者: 刻苦讀書 時間: 2025-3-25 11:26 作者: saphenous-vein 時間: 2025-3-25 12:16
Transfer Learning in Mobile Healthting, and ends with a model generation module to carry out the machine learning task in the new platform or with the new user. The chapter uses . to realize the TransFall framework using time-series data.作者: 造反,叛亂 時間: 2025-3-25 18:37
Textbook 2022neural networks, including generative adversarial neural networks and graph convolution networks. Most importantly, this book helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a problem they wish to solve.?In 作者: nominal 時間: 2025-3-25 21:51
data being handled.Includes numerous, practical case-studies.This textbook introduces readers to the theoretical aspects of machine learning (ML) algorithms, starting from simple neuron basics, through complex neural networks, including generative adversarial neural networks and graph convolution ne作者: notice 時間: 2025-3-26 00:58
Setareh Rafatirad,Houman Homayoun,Sai Manoj PudukoDescribes traditional as well as advanced machine learning algorithms.Enables students to learn which algorithm is most appropriate for the data being handled.Includes numerous, practical case-studies作者: 全部 時間: 2025-3-26 05:56
http://image.papertrans.cn/m/image/620590.jpg作者: 飛鏢 時間: 2025-3-26 10:10 作者: 發(fā)現(xiàn) 時間: 2025-3-26 16:31 作者: bibliophile 時間: 2025-3-26 20:27
Machine Learning for Computer Scientists and Data AnalystsFrom an Applied Pers作者: MIR 時間: 2025-3-26 23:46
Machine Learning for Computer Scientists and Data Analysts978-3-030-96756-7作者: EVEN 時間: 2025-3-27 01:44
What Is Applied Machine Learning?to turn data into actionable results and perform a certain task such as detecting a malicious activity in an IoT system, classifying an object in an autonomous driving application, or discovering interesting correlations between variables of patients’ dataset in a health application domain. Machine 作者: Ceramic 時間: 2025-3-27 05:50
SensorNet: An Educational Neural Network Framework for Low-Power Multimodal Data Classificationifferent case studies, and (6) has a very efficient architecture that makes it suitable to be employed at IoT and wearable devices. A custom low-power hardware architecture is also touched upon for the efficient deployment of SensorNet at embedded real-time systems.作者: groggy 時間: 2025-3-27 10:31
Applied Machine Learning for Computer Architecture Security Hence, developing effective cybersecurity countermeasures is of prime interest in mitigating such attacks. Furthermore, recent advancements in the area of machine learning and data mining, motivated by a significant increase in the size of data from high-performance computing systems, have resulted作者: 嘲笑 時間: 2025-3-27 15:12
Applied Machine Learning for Cloud Resource Managementce utilization to achieve cost efficiency. This chapter will discuss how to use Machine Learning (ML) techniques to alleviate the complexity of such tasks. Despite of several advantages of ML-based resource provisioning systems, researchers substantiated that ML brings new security challenges, such 作者: 項目 時間: 2025-3-27 20:29
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: 符合國情 時間: 2025-3-28 01:25
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: grovel 時間: 2025-3-28 05:19 作者: Judicious 時間: 2025-3-28 07:25 作者: Nucleate 時間: 2025-3-28 14:16 作者: 爆炸 時間: 2025-3-28 16:38 作者: Oafishness 時間: 2025-3-28 20:30
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: 捏造 時間: 2025-3-29 01:34
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: 胰臟 時間: 2025-3-29 04:09
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: 積極詞匯 時間: 2025-3-29 08:21 作者: Etymology 時間: 2025-3-29 13:48
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: patriot 時間: 2025-3-29 17:02
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao作者: 環(huán)形 時間: 2025-3-29 21:24
Setareh Rafatirad,Houman Homayoun,Zhiqian Chen,Sai Manoj Pudukotai Dinakarrao
