標題: Titlebook: Machine and Deep Learning Algorithms and Applications; Uday Shankar Shanthamallu,Andreas Spanias Book 2022 Springer Nature Switzerland AG [打印本頁] 作者: metabolism 時間: 2025-3-21 19:07
書目名稱Machine and Deep Learning Algorithms and Applications影響因子(影響力)
書目名稱Machine and Deep Learning Algorithms and Applications影響因子(影響力)學(xué)科排名
書目名稱Machine and Deep Learning Algorithms and Applications網(wǎng)絡(luò)公開度
書目名稱Machine and Deep Learning Algorithms and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Machine and Deep Learning Algorithms and Applications被引頻次
書目名稱Machine and Deep Learning Algorithms and Applications被引頻次學(xué)科排名
書目名稱Machine and Deep Learning Algorithms and Applications年度引用
書目名稱Machine and Deep Learning Algorithms and Applications年度引用學(xué)科排名
書目名稱Machine and Deep Learning Algorithms and Applications讀者反饋
書目名稱Machine and Deep Learning Algorithms and Applications讀者反饋學(xué)科排名
作者: 冥界三河 時間: 2025-3-21 21:26 作者: 疾馳 時間: 2025-3-22 02:04
Machine and Deep Learning Applications,mobile devices with access to cloud computing. While cloud computing provides the necessary computational power to train deep learning models, trained models can be easily deployed in the cloud or on embedded devices at the edge of the cloud to carry out the inference.作者: generic 時間: 2025-3-22 06:49 作者: negotiable 時間: 2025-3-22 10:31 作者: 使熄滅 時間: 2025-3-22 16:35
Supervised Learning,the ground truth for samples contained in the training, validation, and test data sets. Ground truth represents “true” or “correct” labels for the input dataset. Expert help may be needed to obtain the correct labels for the data (medical image labeling, for example). The ML model is “trained” using作者: 記憶 時間: 2025-3-22 19:46 作者: Osteons 時間: 2025-3-22 21:40
Neural Networks and Deep Learning,g, and different architectures. Artificial neural networks are powerful pattern recognition machines, and they have proved to be the most successful. Neural networks and deep learning are quite successful at end-to-end learning, and they do not require feature engineering as in traditional machine l作者: Flatter 時間: 2025-3-23 02:57 作者: 骨 時間: 2025-3-23 08:09
Conclusion and Future Directions,edge and bibliography on machine learning and neural networks concepts to a reader with minimal background in machine learning. We started with the fundamental learning paradigms in ML and explored the sub-categories in each. Supervised learning, unsupervised learning, and semi-supervised learning a作者: absolve 時間: 2025-3-23 09:57
978-3-031-03748-1Springer Nature Switzerland AG 2022作者: Valves 時間: 2025-3-23 17:07 作者: 勾引 時間: 2025-3-23 18:20
Synthesis Lectures on Signal Processinghttp://image.papertrans.cn/m/image/620799.jpg作者: 愉快么 時間: 2025-3-23 23:10
Conclusion and Future Directions,s organized to cover algorithms and concepts first. It later describes the applications of ML algorithms in various fields, including signal processing, image and computer vision, natural language processing, speech and audio processing, energy, health, security, and defense applications.作者: 感情脆弱 時間: 2025-3-24 02:43
Introduction to Machine Learning,rained on thousands of images of dogs and cats until it can learn to distinguish the two. Similarly, for spam email filtering, an ML model can be trained with a lot of benign and spam emails to filter future spam messages.作者: Malleable 時間: 2025-3-24 09:36
Supervised Learning, a labeled input dataset termed . Once the model achieves the desired performance on training data, the trained model is then used to perform inference on unseen data. The data that has not been used for training and thus unseen by the model is termed 作者: tooth-decay 時間: 2025-3-24 12:06 作者: Militia 時間: 2025-3-24 15:44 作者: 單挑 時間: 2025-3-24 22:13
Book 2022. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of作者: BLOT 時間: 2025-3-25 00:28 作者: glans-penis 時間: 2025-3-25 05:54
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