Generative Adversarial Networks with Python Deep Learning Generative Models for Image Synthesis and Image Translation by Jason Brownlee 29 step-by-step lessons, 652 pages. intuitions behind models, much more. generate faces, translate photos, more 生成对抗网络是一种深度学习生成模型,可以在一系列图像合成和图像对图像转换问题上实现惊人的照片现实效果。 在这部新的电子书写在友好的机器学习掌握风格,你习惯了,跳过数学,直接跳到获得结果。 通过清晰的解释、标准的 Python 库(Keras和TensorFlow 2)和分步教程课程,您将发现如何为自己的计算机视觉项目开发生成对抗网络。
2021-06-26 20:02:15 11.19MB GAN 生成对抗网络 deep learning
Long Short-Term Memory Networks With Python Develop Deep Learning Models for your Sequence Prediction Problems by Jason Brownlee 14 step-by-step lessons, 246 pages. 6 LSTM model architectures. 长期短期记忆网络(简称 LSTM)是一种经常性神经网络,可在具有挑战性的预测问题上取得最先进的结果。 在这本以LSTM为中心的电子书中,你习惯了友好的机器学习掌握风格,最后切入了关于 LSTM 的数学、研究论文和综合描述。 使用清晰的解释,标准的Python库(Keras和TensorFlow2)和分步教程课程,您将发现什么是 LSTM,以及如何开发一套 LSTM 模型,以充分利用您的序列预测问题的方法
2021-06-26 16:02:34 6.48MB lstm deep learning ml
Fluke Networks OptiView 链路分析仪(OPV-LA)pdf,Fluke Networks OptiView 链路分析仪(OPV-LA)
2021-06-26 14:47:33 1.55MB 综合资料
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原始帕洛阿尔托网络PSE策略考试最佳准备问题2021- Palo Alto Networks PSE-Strata转储获得成功的重要性是什么? 您应该获得帕洛阿尔托网络系统工程师认证,才能发展信息技术领域的职业。 Palo Alto Networks系统工程师-Strata考试与此证书相关联。 您将需要实际的Palo Alto Networks DumpsKey策略才能在第一次尝试中获得及格分数。 未经充分准备,切勿出现在考试中。 Palo Alto Networks系统工程师认证的注册费非常高。 因此,专业人士推荐更新的Palo Alto Networks PSE-Strata考试转储,以节省金钱和时间 Palo Alto Networks PSE-Strata考试信息: 供应商: Palo Alto Networks 考试代码: PSE-Strata 认证名称: Palo A
2021-06-23 13:56:25 4KB
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09年新书,很好的知无线电 网络中的频谱接入和管理,下面是IEEE的书评: The book is divided into three parts. Part one is a general introduction to wire- less communication systems, reviewing communication architectures and tech- nologies, as well as resource allocation protocols and dynamic spectrum access, discussing features, research challenges, and standardization. Part two is a further discussion on wireless system design with a focus on analysis of dynamic spectrum access systems. A brief introduction to signal processing and optimization tech- niques is presented, as well as basics of game theory and intelligent algorithms (e.g., machine learning, genetic algo- rithms, and fuzzy logic). Finally, part three discusses in detail dynamic spec- trum access and management. Models and architectures of dynamic spectrum access are introduced and described in detail. The authors first present the cen- tralized dynamic spectrum access model, and later focus on the distributed approach. Distributed dynamic spectrum access is discussed from the algorithmic and protocol perspectives in separate chapters. Finally, a spectrum trading model is presented with its applications to wireless communications. This book is a valuable source of information for people new to the con- cept of dynamic spectrum access. Also, scientists and engineers already involved in dynamic spectrum access research will find this book a good reference source. This is one of the few books on the mar- ket related to dynamic spectrum access and cognitive radio written completely by the authors themselves. Therefore, it is very cohesive, has a very good flow, and does not repeat itself while intro- ducing new concepts, as do many edited books on a similar topic available on the market. Many concepts of dynamic spec- trum access are well systematized in the book, and the literature review is very broad and complete. One small draw- back is a bit too lengthy introduction to wireless communication systems in part two of the book (which can e
2021-06-23 09:51:16 3.07MB 感知无线电 Dynamic Spectrum Access
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跟着导师学习,导师让我看行为识别相关的顶会论文.这是我自己做的PPT,内容详细,风格简洁
2021-06-22 19:19:09 1.36MB PPT
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笔记 不能保证所有实现都是正确的,未经原始作者检查,只能从本文描述中重新实现。 原始纸 包含EEGNet的原始论文和模型 tf_EEGNet 这是EEGNet的张量流实现 有关更多信息,请参见 tf_ConvNet 这是ConvNet的tensorflow实现 有关更多信息,请参见 留一题实验 型号:tf_EEGNet BCI_competion 2a的预处理 1. A trial contained 2s and was extraced 0.5s after the cue was given. 2. A 4-38Hz bandpass was done by a causal 6-order Butterworth fliter. 3. The MI dataset was sampled at 250Hz. And it was resampled to 128Hz for E
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贝叶斯神经网络预测:使用动态贝叶斯神经网络预测连续信号数据和Web跟踪数据。 与其他网络架构相比
2021-06-21 21:13:28 10.03MB time-series matlab neural-networks object-tracking
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使用seq2point神经网络进行点对点学习以进行非侵入式负载监测
2021-06-20 14:51:26 599KB NILM
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一本非常经典的卡尔曼滤波教程,适合中高等的研究人员参考
2021-06-19 19:24:06 4.17MB 卡尔曼滤波 神经网络
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