標(biāo)題: Titlebook: Applied Deep Learning; Tools, Techniques, a Paul Fergus,Carl Chalmers Textbook 2022 Springer Nature Switzerland AG 2022 Deep Learning.Machi [打印本頁] 作者: VIRAL 時間: 2025-3-21 19:57
書目名稱Applied Deep Learning影響因子(影響力)
書目名稱Applied Deep Learning影響因子(影響力)學(xué)科排名
書目名稱Applied Deep Learning網(wǎng)絡(luò)公開度
書目名稱Applied Deep Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Applied Deep Learning被引頻次
書目名稱Applied Deep Learning被引頻次學(xué)科排名
書目名稱Applied Deep Learning年度引用
書目名稱Applied Deep Learning年度引用學(xué)科排名
書目名稱Applied Deep Learning讀者反饋
書目名稱Applied Deep Learning讀者反饋學(xué)科排名
作者: 我吃花盤旋 時間: 2025-3-21 22:23 作者: Baffle 時間: 2025-3-22 01:04 作者: VEIL 時間: 2025-3-22 08:20
https://doi.org/10.1007/978-3-658-20367-2Linux (WSL2). This overcomes the limitation of accessing GPUs with NVidia Docker runtime which is covered in this chapter. Currently, the only way to access the NVidia Docker runtime and GPU in Windows is to run WSL2.作者: scoliosis 時間: 2025-3-22 11:06 作者: 安心地散步 時間: 2025-3-22 13:17 作者: 不公開 時間: 2025-3-22 18:08
Deploying and Hosting Machine Learning Modelsentertainment, online shopping and even our healthcare. The advanced machine learning algorithms we see today are?relatively new and consequently how we deploy these algorithms has not fully matured. Until recently this was only undertaken by large tech giants but as you will see in this chapter any作者: cravat 時間: 2025-3-22 23:00
Enterprise Machine Learning ServingLinux (WSL2). This overcomes the limitation of accessing GPUs with NVidia Docker runtime which is covered in this chapter. Currently, the only way to access the NVidia Docker runtime and GPU in Windows is to run WSL2.作者: 薄荷醇 時間: 2025-3-23 03:17
https://doi.org/10.1007/978-3-031-04420-5Deep Learning; Machine Learning; TensorFlow; Neural Networks; Accelerated Learning; RAPIDS (Open GPU Data作者: 先兆 時間: 2025-3-23 06:30
978-3-031-04422-9Springer Nature Switzerland AG 2022作者: 討好美人 時間: 2025-3-23 11:52
Zeitkonstanten in ventilierten Luftwegen,ss cars in the future and even in the fight against combating some of the most challenging medical problems faced by humanity. Many aspects of AI have transitioned from a purely theoretical field to an applied one. Therefore, unlike traditional university courses, this book provides an apprenticeshi作者: jocular 時間: 2025-3-23 17:41 作者: endoscopy 時間: 2025-3-23 21:15
https://doi.org/10.1007/978-3-642-93220-5ning tasks and describe how they work and fit into a supervised learning pipeline. These tasks provide a scaffold to support the development of supervised learning models. This chapter will include data processing, feature engineering and model selection along with example algorithms. The two strand作者: 澄清 時間: 2025-3-24 01:19
https://doi.org/10.1007/978-3-030-27749-9he supervised learning aspect of the previous approach is reliant on labelled data to train a model. In supervised learning, the algorithms are designed to look for patterns within a dataset with no predetermined labels. There are two primary types of unsupervised learning which includes cluster ana作者: Budget 時間: 2025-3-24 03:35 作者: 危險 時間: 2025-3-24 09:27 作者: 使成整體 時間: 2025-3-24 12:26
https://doi.org/10.1007/978-3-030-71722-3s have. The influx of both data and compute capabilities have enabled the rapid growth and adoption of computer vision applications. This is one of the most significant technological revolutions which has demonstrated impact across multiple domains including manufacturing, healthcare, security, and 作者: Vital-Signs 時間: 2025-3-24 18:20
https://doi.org/10.1007/978-3-476-04983-4based data. Time series data plays a significant part in our daily lives. From predicting stocks and shares in the stock market to monitoring the vital statistics of a patient and determining medical outcomes. There are many prediction problems and a common component between them all is time. Some o作者: majestic 時間: 2025-3-24 19:36
https://doi.org/10.1007/978-3-476-04983-4 fundamental component of human intelligence. As with many aspects of AI such as image processing it is not surprising that a whole domain of research, tools and techniques have emerged which enable computers to do something similar. In the early days, a significant amount of research was undertaken作者: 果仁 時間: 2025-3-25 01:02
https://doi.org/10.1007/978-3-031-30422-4lly, the chapter will introduce Autoencoders (AEs) which are primarily used for dimensionality and noise reduction and are a powerful tool in signal processing, image analysis and NLP. The chapter also discusses Generative Adversarial Networks (GANs) which facilitates the synthetic generation of com作者: 除草劑 時間: 2025-3-25 06:24
Linear Boundary Value Problems,]. DRL is primarily used to learn from actions enacted in an environment. This is like how humans learn from experience. This area is seeing rapid development in a broad range of disciplines which include driverless cars, simulation, and gameplay.作者: 賠償 時間: 2025-3-25 07:58
Healthcare Sensing and Monitoring,This is achieved by providing users with the ability to execute end-to-end data science pipelines on GPU’s or large-scale CPU based clusters. Although this is a widespread practice for DL applications, historically the training of traditional machine learning models such as SVM’s and RF’s have been 作者: Palate 時間: 2025-3-25 15:40
https://doi.org/10.1007/978-3-658-20367-2imately, of course, after you have finished experimenting, you will need to consider a more production-friendly environment than your laptop. With the widespread industrial support and investment, this has been made easier through a variety of different frameworks. Tech giants such as Google, Facebo作者: 無聊點好 時間: 2025-3-25 18:58
https://doi.org/10.1007/978-3-658-20367-2can be used in a business pipeline. Access to these models can be direct or through model servers to support enterprise solutions. In the previous chapter, we also discussed how models can be accessed directly through library imports. In this chapter, we will discuss component-based MLOps and how mo作者: allude 時間: 2025-3-25 20:30 作者: 討厭 時間: 2025-3-26 00:19
Linear Boundary Value Problems,]. DRL is primarily used to learn from actions enacted in an environment. This is like how humans learn from experience. This area is seeing rapid development in a broad range of disciplines which include driverless cars, simulation, and gameplay.作者: Brocas-Area 時間: 2025-3-26 07:41 作者: alabaster 時間: 2025-3-26 11:50
Computational Intelligence Methods and Applicationshttp://image.papertrans.cn/a/image/159774.jpg作者: 斜谷 時間: 2025-3-26 15:33
Deep Reinforcement Learning]. DRL is primarily used to learn from actions enacted in an environment. This is like how humans learn from experience. This area is seeing rapid development in a broad range of disciplines which include driverless cars, simulation, and gameplay.作者: 假裝是你 時間: 2025-3-26 20:17
Introductionss cars in the future and even in the fight against combating some of the most challenging medical problems faced by humanity. Many aspects of AI have transitioned from a purely theoretical field to an applied one. Therefore, unlike traditional university courses, this book provides an apprenticeshi作者: dapper 時間: 2025-3-26 23:22 作者: Phenothiazines 時間: 2025-3-27 03:04 作者: negotiable 時間: 2025-3-27 06:37 作者: 幾何學(xué)家 時間: 2025-3-27 09:54 作者: 高歌 時間: 2025-3-27 14:54 作者: 向前變橢圓 時間: 2025-3-27 21:03 作者: 分開 時間: 2025-3-27 22:25
Deep Learning Techniques for Time Series Modellingbased data. Time series data plays a significant part in our daily lives. From predicting stocks and shares in the stock market to monitoring the vital statistics of a patient and determining medical outcomes. There are many prediction problems and a common component between them all is time. Some o作者: SPASM 時間: 2025-3-28 02:39 作者: 夾死提手勢 時間: 2025-3-28 08:08
Deep Generative Modelslly, the chapter will introduce Autoencoders (AEs) which are primarily used for dimensionality and noise reduction and are a powerful tool in signal processing, image analysis and NLP. The chapter also discusses Generative Adversarial Networks (GANs) which facilitates the synthetic generation of com作者: Indolent 時間: 2025-3-28 11:09 作者: 喊叫 時間: 2025-3-28 18:30 作者: 巧辦法 時間: 2025-3-28 20:02
Deploying and Hosting Machine Learning Modelsimately, of course, after you have finished experimenting, you will need to consider a more production-friendly environment than your laptop. With the widespread industrial support and investment, this has been made easier through a variety of different frameworks. Tech giants such as Google, Facebo作者: notion 時間: 2025-3-28 23:20
Enterprise Machine Learning Servingcan be used in a business pipeline. Access to these models can be direct or through model servers to support enterprise solutions. In the previous chapter, we also discussed how models can be accessed directly through library imports. In this chapter, we will discuss component-based MLOps and how mo作者: ARY 時間: 2025-3-29 05:17 作者: 光滑 時間: 2025-3-29 10:47 作者: 有偏見 時間: 2025-3-29 12:55 作者: 有花 時間: 2025-3-29 17:14
https://doi.org/10.1007/978-3-476-04983-4 using symbolic AI to construct and interoperate language using syntax and semantic representations of language. Although these early attempts were impressive for the time, symbolic Natural Language Processing (NLP) failed to deliver anything near human-level abilities.作者: caldron 時間: 2025-3-29 22:02 作者: Aspiration 時間: 2025-3-30 00:46
2510-1765 ssible to everyone regardless of their experience.Provides a.This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise作者: 苦笑 時間: 2025-3-30 06:31
https://doi.org/10.1007/978-3-642-93220-5ised learning models. This chapter will include data processing, feature engineering and model selection along with example algorithms. The two strands of supervised learning which includes classification and regression will also be discussed.作者: 提煉 時間: 2025-3-30 09:50 作者: 無禮回復(fù) 時間: 2025-3-30 14:50
https://doi.org/10.1007/978-3-031-30422-4rocessing, image analysis and NLP. The chapter also discusses Generative Adversarial Networks (GANs) which facilitates the synthetic generation of complex data [1]. This area is seeing rapid development and practical application in image generation.作者: Fillet,Filet 時間: 2025-3-30 18:20 作者: 侵略主義 時間: 2025-3-30 23:48
