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种子名称： [CourseClub.NET] Coursera - Neural Networks and Deep Learning
文件类型： 视频
文件数目： 46个文件
文件大小： 877.57 MB
收录时间： 2019-4-30 15:25
已经下载： 3次
资源热度： 197
最近下载： 2024-12-29 10:09

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[CourseClub.NET] Coursera - Neural Networks and Deep Learning.torrent

001.Welcome to the Deep Learning Specialization/001. Welcome.mp410.21MB

002.Introduction to Deep Learning/002. What is a neural network.mp49.97MB

002.Introduction to Deep Learning/003. Supervised Learning with Neural Networks.mp412.9MB

002.Introduction to Deep Learning/004. Why is Deep Learning taking off.mp418.64MB

002.Introduction to Deep Learning/005. About this Course.mp44.66MB

002.Introduction to Deep Learning/006. Course Resources.mp42.5MB

003.Heroes of Deep Learning (Optional)/007. Geoffrey Hinton interview.mp4191.76MB

004.Logistic Regression as a Neural Network/008. Binary Classification.mp415.24MB

004.Logistic Regression as a Neural Network/009. Logistic Regression.mp48.48MB

004.Logistic Regression as a Neural Network/010. Logistic Regression Cost Function.mp413.19MB

004.Logistic Regression as a Neural Network/011. Gradient Descent.mp417.05MB

004.Logistic Regression as a Neural Network/012. Derivatives.mp413.41MB

004.Logistic Regression as a Neural Network/013. More Derivative Examples.mp416.76MB

004.Logistic Regression as a Neural Network/014. Computation graph.mp45.66MB

004.Logistic Regression as a Neural Network/015. Derivatives with a Computation Graph.mp421.69MB

004.Logistic Regression as a Neural Network/016. Logistic Regression Gradient Descent.mp411.15MB

004.Logistic Regression as a Neural Network/017. Gradient Descent on m Examples.mp412.17MB

005.Python and Vectorization/018. Vectorization.mp412.6MB

005.Python and Vectorization/019. More Vectorization Examples.mp410.34MB

005.Python and Vectorization/020. Vectorizing Logistic Regression.mp411.46MB

005.Python and Vectorization/021. Vectorizing Logistic Regression's Gradient Output.mp415.55MB

005.Python and Vectorization/022. Broadcasting in Python.mp416.17MB

005.Python and Vectorization/023. A note on python numpy vectors.mp412.36MB

005.Python and Vectorization/024. Quick tour of Jupyter iPython Notebooks.mp49.23MB

005.Python and Vectorization/025. Explanation of logistic regression cost function (optional).mp410.47MB

006.Heroes of Deep Learning (Optional)/026. Pieter Abbeel interview.mp480.04MB

007.Shallow Neural Network/027. Neural Networks Overview.mp47.23MB

007.Shallow Neural Network/028. Neural Network Representation.mp48.26MB

007.Shallow Neural Network/029. Computing a Neural Network's Output.mp416.32MB

007.Shallow Neural Network/030. Vectorizing across multiple examples.mp413.86MB

007.Shallow Neural Network/031. Explanation for Vectorized Implementation.mp411.97MB

007.Shallow Neural Network/032. Activation functions.mp419.93MB

007.Shallow Neural Network/033. Why do you need non-linear activation functions.mp49.29MB

007.Shallow Neural Network/034. Derivatives of activation functions.mp411.38MB

007.Shallow Neural Network/035. Gradient descent for Neural Networks.mp416.01MB

007.Shallow Neural Network/036. Backpropagation intuition (optional).mp426.04MB

007.Shallow Neural Network/037. Random Initialization.mp411.96MB

008.Heroes of Deep Learning (Optional)/038. Ian Goodfellow interview.mp454.53MB

009.Deep Neural Network/039. Deep L-layer neural network.mp410.35MB

009.Deep Neural Network/040. Forward Propagation in a Deep Network.mp413.02MB

009.Deep Neural Network/041. Getting your matrix dimensions right.mp417.35MB

009.Deep Neural Network/042. Why deep representations.mp417.59MB

009.Deep Neural Network/043. Building blocks of deep neural networks.mp412.81MB

009.Deep Neural Network/044. Forward and Backward Propagation.mp419.8MB

009.Deep Neural Network/045. Parameters vs Hyperparameters.mp410.21MB

009.Deep Neural Network/046. What does this have to do with the brain.mp46MB