【尊享】ZX025 – AI架构师[32.4G]

【尊享】ZX025 – AI架构师[32.4G]

┣━━00.试看 [388.1M]
┃ ┗━━开班典礼2_.mp4 [388.1M]
┣━━01.视频 [31.7G]
┃ ┣━━第01周 开班典礼 [1.1G]
┃ ┃ ┣━━开班典礼1.vep [298.5M]
┃ ┃ ┣━━开班典礼2.vep [388.1M]
┃ ┃ ┗━━开班典礼3.vep [442.9M]
┃ ┣━━第02周 基础理论及课程介绍 [1.9G]
┃ ┃ ┣━━lecture1.vep [402.5M]
┃ ┃ ┣━━lecture2.vep [378.3M]
┃ ┃ ┣━━review1.vep [388.1M]
┃ ┃ ┣━━review2.vep [406.3M]
┃ ┃ ┗━━review3.vep [350.2M]
┃ ┣━━第03周 并行及分布式框架概述 [1.6G]
┃ ┃ ┣━━01.框架概述 [1.1G]
┃ ┃ ┃ ┣━━并行及分布式框架概述1.vep [299.6M]
┃ ┃ ┃ ┣━━并行及分布式框架概述2.vep [223.6M]
┃ ┃ ┃ ┣━━并行及分布式框架概述3.vep [276.9M]
┃ ┃ ┃ ┣━━并行及分布式框架概述4.vep [263.1M]
┃ ┃ ┃ ┗━━并行及分布式框架概述5.vep [63.5M]
┃ ┃ ┣━━02.代码实战 [332.2M]
┃ ┃ ┃ ┣━━代码实战课程1.vep [85.3M]
┃ ┃ ┃ ┣━━代码实战课程2.vep [78M]
┃ ┃ ┃ ┗━━代码实战课程3.vep [168.9M]
┃ ┃ ┗━━03.阅读paper [207.1M]
┃ ┃ ┣━━如何阅读Paper1.vep [151.3M]
┃ ┃ ┗━━如何阅读Paper2.vep [55.8M]
┃ ┣━━第04周 环境安装与gitlab的使用 [1.6G]
┃ ┃ ┣━━01.环境安装与gitlab的使用 [453.4M]
┃ ┃ ┃ ┣━━环境安装与gitlab的使用1.vep [259.8M]
┃ ┃ ┃ ┗━━环境安装与gitlab的使用2.vep [193.6M]
┃ ┃ ┣━━02.经典并行模式 [551.8M]
┃ ┃ ┃ ┣━━经典并行模式1.vep [349.1M]
┃ ┃ ┃ ┗━━经典并行模式2.vep [202.6M]
┃ ┃ ┗━━03.Speaker-Aware Talking-Head Animation [653.5M]
┃ ┃ ┣━━SpeakerAwareTalkingHeadAnimation1.vep [433.1M]
┃ ┃ ┗━━SpeakerAwareTalkingHeadAnimation2.vep [220.4M]
┃ ┣━━第05周 卷积结构及其计算 [3.3G]
┃ ┃ ┣━━01.卷积结构及其计算 [2G]
┃ ┃ ┃ ┣━━卷积结构及其计算1.vep [562.6M]
┃ ┃ ┃ ┣━━卷积结构及其计算2.vep [502.2M]
┃ ┃ ┃ ┣━━卷积结构及其计算3.vep [503.1M]
┃ ┃ ┃ ┗━━卷积结构及其计算4.vep [525.2M]
┃ ┃ ┣━━02.EFFICIENT WINOGRAD CONVOLUTION VIA INTEGER ARITHMETIC [532.8M]
┃ ┃ ┃ ┣━━EFFICIENTWINOGRADCONVOLUTIONVIAINTEGERARITHMETIC1.vep [214.2M]
┃ ┃ ┃ ┗━━EFFICIENTWINOGRADCONVOLUTIONVIAINTEGERARITHMETIC2.vep [318.5M]
┃ ┃ ┗━━03.前后向算法 [752M]
┃ ┃ ┣━━前后向算法1.vep [374.1M]
┃ ┃ ┗━━前后向算法2.vep [377.9M]
┃ ┣━━第06周 目标检测算法 [1.8G]
┃ ┃ ┣━━01.目标检测算法 [1.3G]
┃ ┃ ┃ ┣━━目标检测算法1.vep [322.5M]
┃ ┃ ┃ ┣━━目标检测算法2.vep [333.3M]
┃ ┃ ┃ ┣━━目标检测算法3.vep [215.4M]
┃ ┃ ┃ ┣━━目标检测算法4.vep [195.7M]
┃ ┃ ┃ ┗━━目标检测算法5.vep [310.8M]
┃ ┃ ┗━━02.Distilling the Knowledge in a Neural Network [511.2M]
┃ ┃ ┗━━DistillingtheKnowledgeinaNeuralNetwork.vep [511.2M]
┃ ┣━━第07周 Lecture1 NvidiaTensort核心算法和Plugin开发 [655.4M]
┃ ┃ ┣━━Lecture1NvidiaTensort核心算法和Plugin开发 [546.7M]
┃ ┃ ┃ ┣━━NvidiaTensort核心算法和Plugin开发-01.vep [125.7M]
┃ ┃ ┃ ┣━━NvidiaTensort核心算法和Plugin开发2.vep [193M]
┃ ┃ ┃ ┣━━NvidiaTensort核心算法和Plugin开发3.vep [110.8M]
┃ ┃ ┃ ┗━━NvidiaTensort核心算法和Plugin开发4.vep [117.1M]
┃ ┃ ┗━━PaperDistillingheKnowledgeinaNeuralNetwork [108.7M]
┃ ┃ ┗━━Paper Distilling the Knowledge in a Neural Network.vep [108.7M]
┃ ┣━━第08周 TensoRT详讲 真实环境下的代码操作 [2G]
┃ ┃ ┣━━DynamicetworkSurgeryorEfficientDNNs [542.9M]
┃ ┃ ┃ ┗━━DynamicNetworkSurgeryforEfficientDNNs.vep [542.9M]
┃ ┃ ┣━━LectureTensoRT详讲真实环境下的代码操作 [933.3M]
┃ ┃ ┃ ┣━━TensoRT详讲真实环境下的代码操作1.vep [196.5M]
┃ ┃ ┃ ┣━━TensoRT详讲真实环境下的代码操作2.vep [254.5M]
┃ ┃ ┃ ┣━━TensoRT详讲真实环境下的代码操作3.vep [205.8M]
┃ ┃ ┃ ┗━━TensoRT详讲真实环境下的代码操作4.vep [276.5M]
┃ ┃ ┗━━TensorRTpluginnmsPlugin这个plugin插件的具体 [546.6M]
┃ ┃ ┣━━插件的具体代码实现1.vep [221.2M]
┃ ┃ ┣━━插件的具体代码实现2.vep [102.4M]
┃ ┃ ┗━━插件的具体代码实现3.vep [223.1M]
┃ ┣━━第09周 个性化语音合成项目技术概览 [2.6G]
┃ ┃ ┣━━Improving Neural Network Quantization without Retraining using [502.5M]
┃ ┃ ┃ ┗━━ImprovingNeuralNetworkQuantization.vep [502.5M]
┃ ┃ ┣━━Lecture 个性化语音合成项目技术概览 [1.4G]
┃ ┃ ┃ ┣━━个性化语音合成项目技术概览1.vep [445.2M]
┃ ┃ ┃ ┣━━个性化语音合成项目技术概览2.vep [565.9M]
┃ ┃ ┃ ┗━━个性化语音合成项目技术概览3.vep [469.5M]
┃ ┃ ┗━━TensorRT SSD 推理 [680.6M]
┃ ┃ ┣━━TensorRTSSD推理1.vep [336.7M]
┃ ┃ ┗━━TensorRTSSD推理2.vep [344M]
┃ ┣━━第10周 计算图表示及优化 [2.4G]
┃ ┃ ┣━━计算图表示及优化 [1.9G]
┃ ┃ ┃ ┣━━计算图表示及优化1.vep [476.7M]
┃ ┃ ┃ ┣━━计算图表示及优化2.vep [438.6M]
┃ ┃ ┃ ┣━━计算图表示及优化3.vep [585M]
┃ ┃ ┃ ┗━━计算图表示及优化4.vep [407M]
┃ ┃ ┗━━WorkshopData-Free Knowledge Distillation for Deep Neural Networks [512.1M]
┃ ┃ ┗━━DataFreeKnowledgeDistillationforDeepNeuralNetworks.vep [512.1M]
┃ ┣━━第11周 Attention-Based Seq2Seq模型Tacotron2-Lecture [1.6G]
┃ ┃ ┣━━1-Lecture Attention-Based Seq2Seq模型Tacotron2 [1G]
┃ ┃ ┃ ┣━━AttentionBasedSeq2Seq模型Tacotron21.vep [215.8M]
┃ ┃ ┃ ┣━━AttentionBasedSeq2Seq模型Tacotron22.vep [426.9M]
┃ ┃ ┃ ┣━━AttentionBasedSeq2Seq模型Tacotron23.vep [211.6M]
┃ ┃ ┃ ┗━━AttentionBasedSeq2Seq模型Tacotron240.vep [194.6M]
┃ ┃ ┣━━2-Workshop 说话人特征提取技术实现及环境搭建 [249.8M]
┃ ┃ ┃ ┣━━说话人特征提取技术实现及环境搭建1.vep [97.3M]
┃ ┃ ┃ ┗━━说话人特征提取技术实现及环境搭建2.vep [152.5M]
┃ ┃ ┣━━3-Workshop Rethinking the Smaller [254.9M]
┃ ┃ ┃ ┣━━RethinkingtheSmaller1.vep [102.5M]
┃ ┃ ┃ ┗━━RethinkingtheSmaller2.vep [152.4M]
┃ ┃ ┗━━4-Workshop 代码练习 [86.8M]
┃ ┃ ┗━━代码练习1.vep [86.8M]
┃ ┣━━第12周 声码器Wave序列生成算法实战-Lecture [2.1G]
┃ ┃ ┣━━声码器Wave序列生成算法实战1.vep [261.3M]
┃ ┃ ┣━━声码器Wave序列生成算法实战2.vep [425.7M]
┃ ┃ ┣━━声码器Wave序列生成算法实战3.vep [296.9M]
┃ ┃ ┣━━声码器Wave序列生成算法实战4.vep [375.6M]
┃ ┃ ┣━━PAYINGMOREATTENTIONTOATTENTIONIMPROVINGTHEPERFORMANCE.vep [189.3M]
┃ ┃ ┣━━Tacotron2合成模型实现1.vep [101.6M]
┃ ┃ ┗━━Tacotron2合成模型实现2.vep [489M]
┃ ┣━━第13周 tensorrt实战 [1.2G]
┃ ┃ ┣━━PerformanceGuaranteedNetworkAccelerationviaHighOrderResidualQuantization.vep [69.3M]
┃ ┃ ┣━━tensorrt实战1.vep [181.1M]
┃ ┃ ┣━━tensorrt实战2.vep [238.7M]
┃ ┃ ┣━━tensorrt实战3.vep [142.8M]
┃ ┃ ┣━━tensorrt实战4.vep [312.4M]
┃ ┃ ┗━━tensorrt实战5.vep [287.3M]
┃ ┣━━第14周 推荐系统概览 [1.2G]
┃ ┃ ┣━━常用Attention的实现1.vep [41.1M]
┃ ┃ ┣━━常用Attention的实现2.vep [155.1M]
┃ ┃ ┣━━推荐系统概览1.vep [163.7M]
┃ ┃ ┣━━推荐系统概览2.vep [124.7M]
┃ ┃ ┣━━推荐系统概览3.vep [198.8M]
┃ ┃ ┣━━推荐系统概览4.vep [233.7M]
┃ ┃ ┣━━作业讲解1.vep [49.6M]
┃ ┃ ┣━━作业讲解2.vep [159.6M]
┃ ┃ ┗━━SimpleResourceConstrainedStructureLearningofDeepNetworks.vep [115.7M]
┃ ┣━━第15周 分布式参数服务器 [860.5M]
┃ ┃ ┣━━分布式参数服务器1.vep [134.6M]
┃ ┃ ┣━━分布式参数服务器2.vep [132.1M]
┃ ┃ ┣━━分布式参数服务器3.vep [178M]
┃ ┃ ┣━━分布式参数服务器4.vep [155.5M]
┃ ┃ ┣━━HorovodfastandeasydistributeddeeplearninginTensorFlow.vep [78.8M]
┃ ┃ ┣━━Horovodtensorflow应用1.vep [43.2M]
┃ ┃ ┗━━Horovodtensorflow应用2.vep [138.5M]
┃ ┣━━第16周 分布式推荐系统实战 [2.1G]
┃ ┃ ┣━━分布式推荐系统实战1.vep [96.6M]
┃ ┃ ┣━━分布式推荐系统实战2.vep [86.9M]
┃ ┃ ┣━━分布式推荐系统实战3.vep [110.4M]
┃ ┃ ┣━━分布式推荐系统实战4.vep [69.4M]
┃ ┃ ┣━━分布式推荐系统实战5.vep [146.8M]
┃ ┃ ┣━━深度学习框架技术-1.vep [292M]
┃ ┃ ┣━━深度学习框架技术-2.vep [282.7M]
┃ ┃ ┣━━深度学习框架技术-3.vep [295.5M]
┃ ┃ ┣━━深度学习框架技术-4.vep [235.7M]
┃ ┃ ┣━━Difacto中SGD算法的实现1.vep [109.1M]
┃ ┃ ┣━━Difacto中SGD算法的实现2.vep [76M]
┃ ┃ ┣━━DistributedTrainingStrategiesfortheStructuredPerceptron.vep [132.9M]
┃ ┃ ┗━━Mixed Precision Training.vep [235M]
┃ ┣━━第17周 训练加速高级技术1 [1.8G]
┃ ┃ ┣━━训练及预测加速高级技术-1.vep [122.1M]
┃ ┃ ┣━━训练及预测加速高级技术-2.vep [257.7M]
┃ ┃ ┣━━训练及预测加速高级技术-3.vep [174.2M]
┃ ┃ ┣━━训练及预测加速高级技术-4.vep [181.5M]
┃ ┃ ┣━━训练及预测加速高级技术-5.vep [145.3M]
┃ ┃ ┣━━训练及预测加速高级技术-6.vep [273.1M]
┃ ┃ ┣━━Local SGD Converges Fast and Communicates Little.vep [289.5M]
┃ ┃ ┣━━TNN高效存储管理机制-1.vep [287.3M]
┃ ┃ ┗━━TNN高效存储管理机制-2.vep [152.8M]
┃ ┣━━第18周 训练及预测加速高级技术 [1.5G]
┃ ┃ ┣━━模型压缩技术实现-1.vep [168.9M]
┃ ┃ ┣━━模型压缩技术实现-2.vep [127.7M]
┃ ┃ ┣━━训练加速高级技术1-1.vep [202M]
┃ ┃ ┣━━训练加速高级技术1-2.vep [113.9M]
┃ ┃ ┣━━训练加速高级技术1-3.vep [200.8M]
┃ ┃ ┣━━训练加速高级技术1-4.vep [187.7M]
┃ ┃ ┣━━训练加速高级技术1-5.vep [283.4M]
┃ ┃ ┗━━训练加速高级技术1-6.vep [222.7M]
┃ ┗━━第19周 最后一课 [421.9M]
┃ ┣━━最后一课-1.vep [60.7M]
┃ ┣━━最后一课-2.vep [69.1M]
┃ ┗━━Fast Locality Sensitive Hashing for Beam Search on GPU.vep [292M]
┗━━00.资料.zip [274.7M]

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