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Ch02安装
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Ch04 The Preliminaries: A Crashcourse
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Ch05 线性神经网络
- 5.1 线性回归
- 5.2 零开始的线性回归实现
- 5.3 线性回归的简洁实现
- 5.4 Softmax回归
- 5.5 图像分类数据(Fashion-MNIST))
- 5.6 从零开始实现Softmax回归
- 5.7 Softmax回归的简洁实现
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Ch06 多层感知器
- 6.1 Multilayer Perceptron
- 6.2 Implementation of Multilayer Perceptron from Scratch
- 6.3 Concise Implementation of Multilayer Perceptron
- 6.4 Model Selection Underfitting and Overfitting
- 6.5 Weight Decay
- 6.6 Dropout
- 6.7 Forward Propagation Backward Propagation and Computational Graphs
- 6.8 Numerical Stability and Initialization
- 6.9 Considering the Environment
- 6.10 Predicting House Prices on Kaggle
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Ch07 深度学习计算
- 7.1 Layers and Blocks
- 7.2 Parameter Management
- 7.3 Deferred Initialization
- 7.4 Custom Layers
- 7.5 File I/O
- 7.6 GPUs
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Ch08 卷积神经网络
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Ch09 现代卷积网络
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Ch10 循环神经网络
- 10.1 Sequence Models
- 10.2 Language Models
- 10.3 Recurrent Neural Networks
- 10.4 Text Preprocessing
- 10.5 Implementation of Recurrent Neural Networks from Scratch
- 10.6 Concise Implementation of Recurrent Neural Networks
- 10.7 Backpropagation Through Time
- 10.8 Gated Recurrent Units (GRU)
- 10.9 Long Short Term Memory (LSTM)
- 10.10 Deep Recurrent Neural Networks
- 10.11 Bidirectional Recurrent Neural Networks
- 10.12 Machine Translation and DataSets
- 10.13 Encoder-Decoder Architecture
- 10.14 Sequence to Sequence
- 10.15 Beam Search
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Ch11注意力机制
- 11.1 Attention Mechanism
- 11.2 Sequence to Sequence with Attention Mechanism
- 11.3 Transformer
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Ch12 优化算法
- 12.1 Optimization and Deep Learning
- 12.2 Convexity
- 12.3 Gradient Descent
- 12.4 Stochastic Gradient Descent
- 12.5 Mini-batch Stochastic Gradient Descent
- 12.6 Momentum
- 12.7 Adagrad
- 12.8 RMSProp
- 12.9 Adadelta
- 12.10 Adam
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Ch14 计算机视觉
- 14.1 Image Augmentation
- 14.2 Fine Tuning
- 14.3 Object Detection and Bounding Boxes
- 14.4 Anchor Boxes
- 14.5 Multiscale Object Detection
- 14.6 Object Detection Data Set (Pikachu)
- 14.7 Single Shot Multibox Detection (SSD)
- 14.8 Region-based CNNs (R-CNNs)
- 14.9 Semantic Segmentation and Data Sets
- 14.10 Transposed Convolution
- 14.11 Fully Convolutional Networks (FCN)
- 14.12 Neural Style Transfer
- 14.13 Image Classification (CIFAR-10) on Kaggle
- 14.14 Dog Breed Identification (ImageNet Dogs) on Kaggle
- 1.基于胶囊网络和迁移学习的锂电池新型图像快速RUL预测.pdf
Novel_Image-Based_Rapid_RUL_Prediction_for_Li-Ion_Batteries_Using_a_Capsule_Network_and_Transfer_Learning.pdf - 2.使用历史状态和未来负载信息与AM-seq2seq模型对锂离子电池进行SOH预测 (2).pdf
- 3.(论文)基于图像和健康指标的迁移学习杂交预测电池RUL.pdf 基于图像和健康指标的迁移学习杂交预测电池RUL1.pdf
- 4.使用历史状态和未来负载信息与AM-seq2seq模型对锂离子电池进行SOH预测 (2).pdf
- 5.多状态影响下基于Bi⁃LSTM网络的锂电池剩余寿命预测方法.pdf 多状态影响下基于Bi⁃LSTM网络的锂电池剩余寿命预测方法总结.pdf
- 6.PyEMD.zip.(code)一种基于模态分解和机器学习的锂电池寿命预测方法_肖浩逸.pdf
- 7.电动汽车锂电池健康状态估计和剩余使用寿命预测的端到端神经网络框架.pdf 基于注意力的深度学习方法的机器剩余使用寿命预测.pdf
* 数字孪生
-中文
-英文
-深度学习
-中文
-英文
* 锂电池技术
-中文
-英文
-论文精读
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[基于数字孪生的铣刀状态实时监控研究_刘明浩(1).pdf]paper
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[油浸式电力变压器匝间故障早期的电热特性研究_张立静(1).pdf]paper
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[重大装备形性一体化数字孪生关键技术_宋学官.pdf]paper
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[基于数字孪生的飞机起落架健康管理技术应用_郭丞皓(1).pdf]paper
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[航天器控制系统智能健康管理技术发展综述_袁利(1).pdf]paper
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[永磁磁浮空轨系统的研究与设计_杨杰.pdf]paper
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[数字孪生在列车曲线通过性能预测中的应用研究_董少迪(1).pdf]paper
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[数字孪生在高速列车生命周期中的应用与挑战_丁国富.pdf]paper
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[基于孪生卷积网络的高速列车转向架故障辨识_吴昀璞.pdf]paper
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[高速磁浮列车悬浮系统性能优化问题研究.pdf]paper
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[基于数字孪生的地铁列车性能评估系统_樊孟杰.pdf]paper
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[矿山数字孪生构建方法与演化机理_张帆(1).pdf]paper
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[未来装备探索:数字孪生装备_陶飞.pdf]paper
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[光纤二次套塑车间数字孪生系统的构建与应用_袁标(1).pdf]paper
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[基于数字孪生的AR多人协作装配方法_丁志昆(1).pdf]paper
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[基于数字孪生的复杂产品装配建模与精度分析方法.pdf]paper
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[数字孪生及其应用探索_陶飞.pdf]paper
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[数字孪生技术综述与展望_刘大同.pdf]paper
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[数字孪生演进模型及其在智能制造中的应用_江海凡.pdf]paper
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[碱性电解槽制氢设备数字孪生体构建及应用_江悦.pdf]paper
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[油浸式电力变压器匝间故障早期的电热特性研究_张立静.pdf][paper][paper]paper
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[腰椎数字孪生实时监测_-_xiwang_he.pdf]paper
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[超燃冲压发动机仿真_从数值飞行到数智飞行_孙明波.pdf]paper
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[工业锅炉数字化设计与数字孪生综述_程浙武 (3).pdf]paper
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[铝颗粒粉尘对冲火焰数值模拟研究_张家瑞.pdf]paper
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[三维、动态、实时数字化锅炉技术发展探讨_郑树.pdf]paper
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[数字孪生在航空发动机燃烧室设计阶段的应用与展望_任祝寅.pdf]paper
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[航空发动机主燃烧室动态燃烧研究现状及关键技术分析_王波.pdf]paper
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[Digital twin and cloud-side-end collaboration for intelligent batterty management system.pdf]paper
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[Establishing a reliable mechanism model of the digital twin machining system: An adaptive evaluatioon network apppriach.pdf]paper
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[A digital twin dosng system for iron reverse flotationDigital twin modeling-main.pdf]paper
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[Digital twin modeling.pdf]paper
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[Digital Twin Technology--A bibliometric study of top research articles based on Local Citataion Scroe.pdf]paper
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[A digital twin-driven trajectory tracking control method of a lower-limbexoskeleton.pdf]paper
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[Artificial intelligence and digital twins in power systems Trends, synergies and opportunities.pdf]paper
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[Intelligentdigital twins and the development and management of complex systems.pdf]paper
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[深度学习增强的数字孪生,用于可视化焊缝生长监测和渗透控制.pdf]paper
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[가상현실에서효과적인3차원영상연출을위한연구언리얼엔진의영상제작을이용한인터렉티브쇼트중심으로.pdf]paper
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[Combustion machine learning Principles, progress and prospects.pdf]paper
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[Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion.pdf]paper
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[Deep learning for presumed probability density function models.pdf]paper
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[Machine learning for combustion.pdf]paper
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[Large eddy simulation of spray combustion using flamelet generated manifolds combined with artificial neural networks.pdf]paper
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[Application of machine learning for filtered density function closure in MILD.pdf]paper
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[Detection of precursors of combustion instability using convolutional recurrent neural networks.pdf]paper
中文
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[采用深度迁移学习与自适应加权的滚动轴承故障诊断_贾峰.pdf]paper
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[基于ISCNN-LightGBM的轴承故障诊断_张思源.pdf]paper
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[基于多尺度注意力深度强化学...络的行星齿轮箱智能诊断方法_王辉.pdf]paper
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[基于卷积双向长短期网络的轴承故障尺寸估计_刘西洋.pdf]paper
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[基于深度学习和多域决策融合的轴承故障智能诊断技术_林诗麒.pdf]paper
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[基于主动生成式过采样和DSN的轴承故障诊断_李慧芳.pdf]paper
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[面向轴承智能诊断的多领域深度对抗迁移网络_贾峰.pdf]paper
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[数据驱动的工业智能:现状与展望_任磊.pdf]paper
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[基于时序关联矩阵的高炉冶炼过程多重关联时延估计方法_蒋珂.pdf]paper
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[深度学习在流程工业过程数据建模中的应用_袁小锋.pdf]paper
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[有色金属工业智能模型库构建方法及应用_阳春华.pdf]paper
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[知识驱动的流程工业智能制造_桂卫华 (1).pdf]paper
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[高炉铁水质量信息在线检测方法综述_蒋珂.pdf]paper
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[基于动态注意力深度迁移网络...高炉铁水硅含量在线预测方法_蒋珂.pdf]paper
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[动态条件下基于深度学习的锂电池容量估计_毕贵红.pdf]paper
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[多状态影响下基于Bi-LS...络的锂电池剩余寿命预测方法_张浩.pdf]paper
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[基于LSTM的锂电池储能装置SOC与SOH联合预测_刘运鑫.pdf]paper
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[基于ZYNQ深度学习模型部署的锂电池健康预测_马贵君.pdf]paper
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[基于多尺度分解和深度学习的锂电池寿命预测_胡天中.pdf]paper
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[超参数优化算法.pdf]paper
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[Forecasting of iron ore sintering quality index A latent variable method with deep inner structure(1).pdf]paper
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[Closed-loop optimization of fast-charging protocols for batteries with machine learning..pdf]paper
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[Data-driven prediction of battery cycle life before capacity degradation.pdf]paper
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[Deep-Learning-Enabled Crack Detection and Analysis in Commercial Lithium-Ion Battery Cathodes..pdf]paper
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[Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning.pdf]paper
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[Identifying degradation patterns of lithium ionbatteries from impedance spectroscopy usingmachine learning.pdf]paper
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[Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation.pdf]paper
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[Impedance-based forecasting of lithium-ion battery performance amid uneven usage.pdf]paper
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[Autonomous optimization of non-aqueous Li-ion battery electrolytesvia roboticexperimentation and machine learning coupling.pdf]paper
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[基于机器学习的锂离子电池健康状态分类与预测_高昊天.pdf]paper
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[基于数据驱动的锂离子电池RUL预测综述_张若可.pdf]paper
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[基于信息熵与PSO-LST...的锂电池组健康状态估计方法_张朝龙.pdf]paper
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[基于序列贝叶斯更新的锂电池剩余寿命预测_赵斐.pdf]paper
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[锂离子电池状态估计机器学习方法综述_谢奕展.pdf]paper
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[模型与数据双驱动的锂电池状态精准估计_陈清炀.pdf]paper
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[深度学习在化学流程工业故障诊断的研究进展_陈红花.pdf]paper
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[一种编解码器模型的锂离子电池健康状态估算_刘昊天.pdf]paper
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[一种基于模态分解和机器学习的锂电池寿命预测方法_肖浩逸.pdf]paper
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[机器学习在固体氧化物燃料电...)电还原催化剂中的研究进展_许建兵.pdf]paper
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[质子交换膜燃料电池系统数字孪生故障诊断模型研究_朱静.pdf]paper
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[面向智能化管理的数字孪生电池构建方法_杨世春.pdf]paper
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[空间电源数字孪生系统_朱凯.pdf]paper
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[基于数值孪生模型的锂空气电池放电特性分析_张添昱.pdf]paper
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[Digital twin for battery systems Cloud battery management system with online state-of-charge and state-of-health estimation.pdf]paper
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[Battery digital twins Perspectives on the fusion of models, data and artificial intelligence for smart battery management systems.pdf]paper
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[An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries.pdf]paper
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[容量退化前电池循环寿命的数据驱动预测.pdf]paper