標(biāo)題: Titlebook: Computer Vision Projects with PyTorch; Design and Develop P Akshay Kulkarni,Adarsha Shivananda,Nitin Ranjan Sh Book 2022 Akshay Kulkarni, A [打印本頁] 作者: obsess 時(shí)間: 2025-3-21 18:38
書目名稱Computer Vision Projects with PyTorch影響因子(影響力)
書目名稱Computer Vision Projects with PyTorch影響因子(影響力)學(xué)科排名
書目名稱Computer Vision Projects with PyTorch網(wǎng)絡(luò)公開度
書目名稱Computer Vision Projects with PyTorch網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Computer Vision Projects with PyTorch被引頻次
書目名稱Computer Vision Projects with PyTorch被引頻次學(xué)科排名
書目名稱Computer Vision Projects with PyTorch年度引用
書目名稱Computer Vision Projects with PyTorch年度引用學(xué)科排名
書目名稱Computer Vision Projects with PyTorch讀者反饋
書目名稱Computer Vision Projects with PyTorch讀者反饋學(xué)科排名
作者: flamboyant 時(shí)間: 2025-3-21 21:02
Cooperation with Microbiologists,Human pose estimation (HPE) is a computer vision task that detects human poses by estimating major keypoints, such as eyes, ears, hands, and legs, in a given frame/video. Figure 6-1 shows an example of human pose estimation in action.作者: 撫育 時(shí)間: 2025-3-22 03:45 作者: 精美食品 時(shí)間: 2025-3-22 06:33 作者: irreparable 時(shí)間: 2025-3-22 08:50 作者: 有常識(shí) 時(shí)間: 2025-3-22 13:37
Video Analytics,The machine learning journey started from structured data long ago to the process of extracting meaningful predictions. As data grew, machine learning started exploring other data types as well. Today, there is no limit to the types of data that can be processed.作者: 有常識(shí) 時(shí)間: 2025-3-22 18:08
Akshay Kulkarni,Adarsha Shivananda,Nitin Ranjan ShIncludes a variety of hands-on computer vision projects using transfer learning and PyTorch.Explains image similarity and anomaly detection models in computer vision.Covers explainable AI for computer作者: 逢迎白雪 時(shí)間: 2025-3-22 22:47 作者: BUST 時(shí)間: 2025-3-23 02:08
https://doi.org/10.1007/978-3-540-48348-9s well, so it is time to practice those. This chapter sets the tone for multiple tasks in the field of computer vision. We start with a basic explanation of how to start using the Torch components to build a model, define a loss function, and train.作者: 攤位 時(shí)間: 2025-3-23 08:08 作者: 抱狗不敢前 時(shí)間: 2025-3-23 10:33
Cooperation with Microbiologists,by the human eye and brain, but is still a difficult problem for computers. Image segmentation is a problem set wherein we try to train computers to understand images so that they can separate dissimilar objects and unite similar objects. This can be in the form of similar pixel intensities or similar textures and shapes.作者: LAVE 時(shí)間: 2025-3-23 15:29 作者: 自負(fù)的人 時(shí)間: 2025-3-23 22:02
978-1-4842-8272-4Akshay Kulkarni, Adarsha Shivananda, and Nitin Ranjan Sharma 2022作者: 錯(cuò)事 時(shí)間: 2025-3-24 00:04 作者: Delirium 時(shí)間: 2025-3-24 02:42
https://doi.org/10.1007/978-3-540-48348-9s well, so it is time to practice those. This chapter sets the tone for multiple tasks in the field of computer vision. We start with a basic explanation of how to start using the Torch components to build a model, define a loss function, and train.作者: 失眠癥 時(shí)間: 2025-3-24 09:48
Cooperation with Microbiologists, part of the problem. The other part lies in the localization of the object. Object detection helps identify the class location of an image with a bounding box. The bounding box can be further processed for various sub-tasks. As an example, think about what a traffic cam needs to detect and identify作者: Allergic 時(shí)間: 2025-3-24 11:39 作者: disparage 時(shí)間: 2025-3-24 17:20
Principles of Antibiotic Therapy,sed environments. Predictive power often follows the model training process. It is an important question that we need to ask often when we are training a model. There is another question that needs an answer—how much data is sufficient to help the model understand the distribution such that we can h作者: Palliation 時(shí)間: 2025-3-24 20:06 作者: acquisition 時(shí)間: 2025-3-25 01:45
https://doi.org/10.1007/978-3-540-48348-9d increasing state-of-the-art models, the current model evaluation is based on accuracy scores. This makes machine learning and deep learning black-box models. This leads to lack of confidence in applying the model and lack of trust of the generated results. There are multiple libraries that help us作者: 膠狀 時(shí)間: 2025-3-25 05:51 作者: fatuity 時(shí)間: 2025-3-25 08:41 作者: PANT 時(shí)間: 2025-3-25 15:27 作者: 逃避系列單詞 時(shí)間: 2025-3-25 17:52 作者: 詳細(xì)目錄 時(shí)間: 2025-3-25 20:49
Book 2022hms and their applications using PyTorch..The book begins with the fundamentals of computer vision: convolutional neural nets, RESNET, YOLO, data augmentation, and other regularization techniques used in the industry. And then it gives you a quick overview of the PyTorch libraries used in the book. 作者: 狂怒 時(shí)間: 2025-3-26 03:58 作者: 一條卷發(fā) 時(shí)間: 2025-3-26 07:40
models in computer vision.Covers explainable AI for computerDesign and develop end-to-end, production-grade computer vision projects for real-world industry problems. This book discusses computer vision algorithms and their applications using PyTorch..The book begins with the fundamentals of compute作者: 水獺 時(shí)間: 2025-3-26 11:30
Principles of Antibiotic Therapy,ngs. We use the IQ score to quantify human intelligence, but what about machines? Machines are also part of this evolutionary journey. How have we moved our focus to machines and made them intelligent, as we know them today? Let’s take a quick look at this history.作者: 靦腆 時(shí)間: 2025-3-26 15:01
Principles of Antibiotic Therapy,g a model. There is another question that needs an answer—how much data is sufficient to help the model understand the distribution such that we can have a good representation? This chapter will work out an example and the concepts regarding these important questions. We are discussing anomaly detection in computer vision.作者: 大火 時(shí)間: 2025-3-26 18:21 作者: 虛弱 時(shí)間: 2025-3-26 23:28
The Building Blocks of Computer Vision,ngs. We use the IQ score to quantify human intelligence, but what about machines? Machines are also part of this evolutionary journey. How have we moved our focus to machines and made them intelligent, as we know them today? Let’s take a quick look at this history.作者: nerve-sparing 時(shí)間: 2025-3-27 01:27 作者: 深淵 時(shí)間: 2025-3-27 07:26 作者: Instantaneous 時(shí)間: 2025-3-27 11:28
The Building Blocks of Computer Vision,her IQ than the average person born in the previous century. Human intelligence allows us to learn, decide, and make new decisions based on our learnings. We use the IQ score to quantify human intelligence, but what about machines? Machines are also part of this evolutionary journey. How have we mov作者: 允許 時(shí)間: 2025-3-27 15:09 作者: Madrigal 時(shí)間: 2025-3-27 21:35 作者: membrane 時(shí)間: 2025-3-28 00:21 作者: 無畏 時(shí)間: 2025-3-28 03:15
Image Anomaly Detection,sed environments. Predictive power often follows the model training process. It is an important question that we need to ask often when we are training a model. There is another question that needs an answer—how much data is sufficient to help the model understand the distribution such that we can h作者: dainty 時(shí)間: 2025-3-28 06:38 作者: Antagonist 時(shí)間: 2025-3-28 11:53 作者: 圣歌 時(shí)間: 2025-3-28 17:11
Book 2022eworks using optimized techniques with highlights on model AI explainability..After reading this book, you will be able to build your own computer vision projects using transfer learning and PyTorch..What You Will Learn.Solve problems in computer vision with PyTorch..Implement transfer learning and 作者: ascetic 時(shí)間: 2025-3-28 19:24 作者: 聯(lián)想記憶 時(shí)間: 2025-3-28 23:34 作者: 輕彈 時(shí)間: 2025-3-29 06:44
,Berlin auf dem Weg zur Dienstleistungsmetropole ? Wirtschaftsstruktur und Besch?ftigungsentwicklungtropole gepr?gt, auf der anderen Seite durch Massenarbeitslosigkeit und Erscheinungen sozialer Polarisierung gekennzeichnet. Der Umbruch seit 1990 hat, was die wirtschaftliche Entwicklung Berlins angeht, viele überzogene Erwartungen unerfüllt gelassen, und eher die kritischen Erwartungen einer Entwi作者: ANTE 時(shí)間: 2025-3-29 10:22 作者: 確定方向 時(shí)間: 2025-3-29 15:22
B. B?ttcher,F. Edler von Koch,B. Tothdetermine whether sufficient magnetic field penetration could be obtained within a vehicle to allow NQR to be used to detect materials concealed inside . This was shown to be feas ible at the resonance frequencies ofammonium nitrate (AN), which was chosen because it has been used as the major consti
