標(biāo)題: Titlebook: Deep Learning with Azure; Building and Deployi Mathew Salvaris,Danielle Dean,Wee Hyong Tok Book 2018 Mathew Salvaris, Danielle Dean, Wee Hy [打印本頁(yè)] 作者: charity 時(shí)間: 2025-3-21 16:26
書目名稱Deep Learning with Azure影響因子(影響力)
書目名稱Deep Learning with Azure影響因子(影響力)學(xué)科排名
書目名稱Deep Learning with Azure網(wǎng)絡(luò)公開度
書目名稱Deep Learning with Azure網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning with Azure被引頻次
書目名稱Deep Learning with Azure被引頻次學(xué)科排名
書目名稱Deep Learning with Azure年度引用
書目名稱Deep Learning with Azure年度引用學(xué)科排名
書目名稱Deep Learning with Azure讀者反饋
書目名稱Deep Learning with Azure讀者反饋學(xué)科排名
作者: Instinctive 時(shí)間: 2025-3-21 20:47 作者: ANT 時(shí)間: 2025-3-22 03:14
https://doi.org/10.1007/978-1-4757-4949-6In Chapter 1, we gave an overview of AI and the basic idea behind deep learning. We discussed how deep learning—applying artificial neural network models with a large number of layers—has yielded state-of-the art results for several research areas, such as image classification, object detection, speech recognition, and natural language processing.作者: Conserve 時(shí)間: 2025-3-22 07:29 作者: 細(xì)節(jié) 時(shí)間: 2025-3-22 09:53 作者: Vsd168 時(shí)間: 2025-3-22 16:31 作者: Vsd168 時(shí)間: 2025-3-22 17:49
Mathew Salvaris,Danielle Dean,Wee Hyong TokProvides a solid introduction to deep learning concepts, trends, and opportunities.Shows how to perform machine learning and deep learning using the latest tools and technologies on Microsoft AI.Teach作者: 意見一致 時(shí)間: 2025-3-22 22:14 作者: 鬧劇 時(shí)間: 2025-3-23 04:15 作者: Substance 時(shí)間: 2025-3-23 07:11 作者: disrupt 時(shí)間: 2025-3-23 12:57 作者: 埋伏 時(shí)間: 2025-3-23 16:10
Band Structure and Scattering Mechanismsminal work done by Hubel and Wiesel (1962). They discovered that individual neuronal cells in the visual cortex responded only to the presence of visual features such as edges of certain orientations. From their experiments they deduced that the visual cortex contains a hierarchical arrangement of n作者: Commonplace 時(shí)間: 2025-3-23 18:04
Band Structure and Scattering Mechanismsactor that figures out the optimal hidden-state representation of the input (in this case a vector of feature maps) and a classifier (typically a fully connected layer). This chapter focuses on the hidden-state representation of other forms of data and explores RNNs. RNNs are especially useful for a作者: Fecal-Impaction 時(shí)間: 2025-3-24 01:47 作者: Console 時(shí)間: 2025-3-24 05:37 作者: fidelity 時(shí)間: 2025-3-24 08:11
Is Nuclear Deterrence Still Relevant?model does not become useful until it is deployed somewhere and consumed by the end user. This chapter describes the various options available on Azure to deploy your models. We provide general guidelines on what to use and when, but this is by no means an exhaustive guide to the Azure platform. In 作者: 先鋒派 時(shí)間: 2025-3-24 13:05 作者: 顯微鏡 時(shí)間: 2025-3-24 15:07 作者: Glycogen 時(shí)間: 2025-3-24 19:44
Trends in Deep Learningh by covering briefly some of the current limitations of deep learning as well as some other areas of AI that seem to hold promise for future AI applications, and discuss briefly some of the ethical and legal implications of deep learning applications.作者: 牛馬之尿 時(shí)間: 2025-3-25 01:22 作者: inconceivable 時(shí)間: 2025-3-25 05:53
Recurrent Neural Networksy connected layer). This chapter focuses on the hidden-state representation of other forms of data and explores RNNs. RNNs are especially useful for analyzing sequences, which is particularly helpful for natural language processing and time series analysis.作者: 圓木可阻礙 時(shí)間: 2025-3-25 10:42
Generative Adversarial Networkstroduced by Goodfellow et al. (2014), are emerging as a powerful new approach toward teaching computers how to do complex tasks through a generative process. As noted by Yann LeCun (at .), GANs are truly the “coolest idea in machine learning in the last 20 years.”作者: 骨 時(shí)間: 2025-3-25 15:42 作者: evince 時(shí)間: 2025-3-25 19:51
Connes-Narnhofer-Thirring Entropy, compute an outcome based on human-programed rules. Computers are extremely useful for mundane operations such as arithmetic calculations, and the speed and scale at which they can tackle these problems has greatly increased over time.作者: Cerumen 時(shí)間: 2025-3-25 20:42 作者: 變化無(wú)常 時(shí)間: 2025-3-26 01:27
Coordinate Systems and Systems of Equationt you use rather than what you own. For more details on the broader Azure Platform, please see the e-book . (Crump & Luijbregts, 2017). The Microsoft AI Platform enables data scientists and developers to create AI solutions in an efficient and cost-effective manner.作者: 雜役 時(shí)間: 2025-3-26 06:55
Band Structure and Scattering Mechanismsy connected layer). This chapter focuses on the hidden-state representation of other forms of data and explores RNNs. RNNs are especially useful for analyzing sequences, which is particularly helpful for natural language processing and time series analysis.作者: Phenothiazines 時(shí)間: 2025-3-26 10:45 作者: 討好女人 時(shí)間: 2025-3-26 15:22
Kamaal T. Jabbour,E. Paul Ratazzicomputing environment. In this chapter, we extend to other training options such as Batch AI and Batch Shipyard, which can both be useful for scaling up or scaling out training. We finish by highlighting briefly some of the other methods of training AI models on Azure that are not as common but might be useful depending on the problem at hand.作者: ASTER 時(shí)間: 2025-3-26 19:27 作者: BRACE 時(shí)間: 2025-3-26 22:59
Is Nuclear Deterrence Still Relevant?get an idea of how they would fit into a larger solution. We also provide a step-by-step tutorial for deployment of a CNN to Azure Kubernetes Services (AKS) with GPU nodes as a hands-on guide for one recommended option for building a real-time request–response AI system.作者: auxiliary 時(shí)間: 2025-3-27 03:05 作者: intangibility 時(shí)間: 2025-3-27 07:11 作者: 連接 時(shí)間: 2025-3-27 11:38 作者: 濃縮 時(shí)間: 2025-3-27 14:53
Microsoft AI Platform by AI. The Microsoft AI Platform runs on the Microsoft Azure cloud computing environment, which provides computing as a utility where you pay for what you use rather than what you own. For more details on the broader Azure Platform, please see the e-book . (Crump & Luijbregts, 2017). The Microsoft 作者: MANIA 時(shí)間: 2025-3-27 20:10
Convolutional Neural Networksminal work done by Hubel and Wiesel (1962). They discovered that individual neuronal cells in the visual cortex responded only to the presence of visual features such as edges of certain orientations. From their experiments they deduced that the visual cortex contains a hierarchical arrangement of n作者: 蝕刻術(shù) 時(shí)間: 2025-3-28 01:53
Recurrent Neural Networksactor that figures out the optimal hidden-state representation of the input (in this case a vector of feature maps) and a classifier (typically a fully connected layer). This chapter focuses on the hidden-state representation of other forms of data and explores RNNs. RNNs are especially useful for a作者: 膽汁 時(shí)間: 2025-3-28 06:10
Generative Adversarial Networkscation to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al. (2014), are emerging as a powerful new approach toward teaching computers how to do complex tasks through a generative p作者: Devastate 時(shí)間: 2025-3-28 09:28
Training AI Modelsre larger. That is why if you are serious about deep learning you have to have access to GPUs. In Azure there are a number of ways you can make use of GPUs, on single VMs or in orchestrated clusters of them. In this chapter, we summarize several of the most common methods available as well as the pr作者: 亞當(dāng)心理陰影 時(shí)間: 2025-3-28 14:18
Operationalizing AI Modelsmodel does not become useful until it is deployed somewhere and consumed by the end user. This chapter describes the various options available on Azure to deploy your models. We provide general guidelines on what to use and when, but this is by no means an exhaustive guide to the Azure platform. In 作者: Limited 時(shí)間: 2025-3-28 16:27 作者: 細(xì)絲 時(shí)間: 2025-3-28 22:49 作者: 壓碎 時(shí)間: 2025-3-29 01:17
Azure Machine Learning services and Batch AI.Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more).Understand the common deep learning models, including convolution978-1-4842-3678-9978-1-4842-3679-6作者: Vertical 時(shí)間: 2025-3-29 05:44 作者: Accolade 時(shí)間: 2025-3-29 07:47
https://doi.org/10.1007/978-1-349-10328-7ods of using the Kolmogorov statistic (and other statistics) for testing fit to a distribution. This literature continues with great strength today, after over 50 years, showing no signs of diminishing. It is evident that the ideas set in motion by Kolmogorov are of paramount importance in statistic作者: 阻撓 時(shí)間: 2025-3-29 11:23
Thomas Hubaueric celebrities who visited them at 35 St Martin’s Street, the home of Sir Isaac Newton from 1710 to 1727. The Burneys took up residence there in 1774; it had the great advantage for this opera-loving family of being within short walking distance of London’s principal opera venue, the King’s Theatre 作者: 無(wú)辜 時(shí)間: 2025-3-29 16:19
