標(biāo)題: Titlebook: Recurrent Neural Networks; From Simple to Gated Fathi M. Salem Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusi [打印本頁] 作者: 輕舟 時間: 2025-3-21 17:39
書目名稱Recurrent Neural Networks影響因子(影響力)
作者: 傳授知識 時間: 2025-3-21 22:47 作者: Dappled 時間: 2025-3-22 03:26
https://doi.org/10.1007/978-3-030-89929-5Neural Networks textbook; Deep Learning textbook; Embedded Deep Learning; Neural Networks and Deep Lear作者: Exclaim 時間: 2025-3-22 04:37
978-3-030-89931-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: Deject 時間: 2025-3-22 09:41
inuous surfaces exhibiting aspheric and free-form shapes, while the machining of molds with discontinuous surfaces (prisms, facets, Fresnel structures, etc.) will be discussed in Chapter 6. Molds may be classified according to size, shape, and tolerance requirements. As a mold material for replicati作者: 舊病復(fù)發(fā) 時間: 2025-3-22 16:02
amond tool occurs inevitably. A recently developed solution of this problem is a thermo-chemical treatment of the steel e.g. by custom nitriding or nitrocarburizing. This process leads to a compound layer at the steel surface, in which the iron atoms are bonded to nitride or carbon nitride, a layer 作者: 不可接觸 時間: 2025-3-22 17:36 作者: 辯論 時間: 2025-3-23 00:27 作者: 帶來的感覺 時間: 2025-3-23 04:22
Fathi M. Salemposed as the means to fabricate regular micro array structures with high surface quality and controllable form accuracy on the surfaces of those difficult-to-machine materials. First, a V-shaped tip diamond grinding wheel with sharp micro-grain cutting edges was obtained by mechanical truing along V作者: Glucose 時間: 2025-3-23 08:53 作者: braggadocio 時間: 2025-3-23 11:23 作者: negligence 時間: 2025-3-23 14:15 作者: 刺穿 時間: 2025-3-23 18:22
Fathi M. Salemiewed as a sequence of different semiconductor layers grown sucessively along a well defined crystalline growth direction. The carriers’ motion along this growth direction of the heterostructure (hereafter called the z direction) is then strongly modified by the presence of the various thin (≈100?) 作者: invulnerable 時間: 2025-3-24 00:34
ingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled978-3-030-89931-8978-3-030-89929-5作者: jarring 時間: 2025-3-24 04:27 作者: 節(jié)約 時間: 2025-3-24 08:29
rences between the machining of non-ferrous metals and thermo-chemically treated steel alloys are discussed. Finally, examples for the replication of glass and plastic optics with moulding inserts made of thermo-chemically treated steel are given. Therefore, this chapter gives an overview over the w作者: figurine 時間: 2025-3-24 13:42
Fathi M. Salemw type of nanoporous materials which have ultra-low density, high surface area and regular pore structure. Compared with the traditional porous materials like active carbons or zeolites, MOFs demonstrate more flexibility in tuning the structure and surface chemistry, making them excellent candidates作者: 軍械庫 時間: 2025-3-24 15:43 作者: 半身雕像 時間: 2025-3-24 19:53
Fathi M. Salemsite materials. Finally, the micro-ground surface and 3D topographies, micro-groove profile, tip arc radius, tip angle and form accuracy were analyzed and investigated for four different materials. The experimental results show that the arc radius at the bottom of micro-groove on the surface of mono作者: GLOOM 時間: 2025-3-25 00:24
of the V-tip wheel trued with the optimal parameters. The results revealed that the highest shape accuracy was obtained at pulse frequency of 4000?Hz, duty cycle of 50%, and open-circuit voltage of 25?V. As a result, the V-tip wheel obtained the minimum tip angle of 62.4° and the minimum tip radius作者: GULP 時間: 2025-3-25 07:17 作者: 斑駁 時間: 2025-3-25 09:48 作者: BUMP 時間: 2025-3-25 12:01 作者: 魯莽 時間: 2025-3-25 16:47
Recurrent Neural Networks (RNN)sed or unsupervised) on the internal hidden units (or states). This holistic treatment brings systemic depth as well as ease to the process of adaptive learning for recurrent neural networks in general as well as the specific form of the simple/basic RNNs. The adaptive learning parts of this chapter作者: 特征 時間: 2025-3-25 23:01
Gated RNN: The Minimal Gated Unit (MGU) RNNnt, namely MGU2, performed better than MGU RNN on the datasets considered, and thus may be used as an alternate to MGU or GRU in recurrent neural networks in limited compute resource platforms (e.g., edge devices).作者: 失望昨天 時間: 2025-3-26 01:52
Textbook 2022 support for design and training choices. The author’s approach enables strategic co-trainingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled作者: 前兆 時間: 2025-3-26 06:31
Textbook 2022provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT).? This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subje作者: declamation 時間: 2025-3-26 09:37
Network Architectures-layer feedforward networks and transitions to the simple recurrent neural network (sRNN) architecture. Finally, the general form of a single- or multi-branch sequential network is illustrated as composed of diverse compatible layers to form a neural network system.作者: Hiatus 時間: 2025-3-26 12:41
Learning Processesplicability of the SGD to a tractable example of one-layer neural network that leads to the Wiener optimal filter and the historical LSM algorithm. The chapter includes two appendices, (i) on what constitutes a gradient system, and (ii) the derivations of the LMS algorithm as the precursor to the backpropagation algorithm.作者: circumvent 時間: 2025-3-26 20:32 作者: animated 時間: 2025-3-27 00:52 作者: 完成 時間: 2025-3-27 04:08
Gated RNN: The Gated Recurrent Unit (GRU) RNN case studies the comparative performance of the standard and the slim GRU RNNs. We evaluate the standard and three . GRU variants on MNIST and IMDB datasets and show that all these GRU RNN perform comparatively.作者: Truculent 時間: 2025-3-27 05:48 作者: Indurate 時間: 2025-3-27 11:38 作者: Sciatica 時間: 2025-3-27 16:29
Recurrent Neural Networks (RNN)end it to a viable architecture, referred it henceforth as the . (bRNN). It follows the architecture presentation with the traditional steps in supervised learning of the . calculations. Using the chain rule from basic calculus, it expresses the calculations into the . (BPTT). The chapter then casts作者: 粉筆 時間: 2025-3-27 19:08
