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种子名称： [CourseClub.NET] Coursera - Introduction to Deep Learning
文件类型： 视频
文件数目： 39个文件
文件大小： 1.27 GB
收录时间： 2019-7-31 19:08
已经下载： 3次
资源热度： 97
最近下载： 2024-12-16 15:08

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[CourseClub.NET] Coursera - Introduction to Deep Learning.torrent

001.Specialization Promo/001. Welcome to AML specialization!.mp413.67MB

002.Course intro/002. Course intro.mp422.1MB

003.Linear model as the simplest neural network/003. Linear regression.mp435.73MB

003.Linear model as the simplest neural network/004. Linear classification.mp442.66MB

003.Linear model as the simplest neural network/005. Gradient descent.mp418.96MB

004.Regularization in machine learning/006. Overfitting problem and model validation.mp426.42MB

004.Regularization in machine learning/007. Model regularization.mp419.85MB

005.Stochastic methods for optimization/008. Stochastic gradient descent.mp421.1MB

005.Stochastic methods for optimization/009. Gradient descent extensions.mp436.57MB

006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp444.68MB

006.The simplest neural network MLP/011. Chain rule.mp426.59MB

006.The simplest neural network MLP/012. Backpropagation.mp431.63MB

007.Matrix derivatives/013. Efficient MLP implementation.mp447.09MB

007.Matrix derivatives/014. Other matrix derivatives.mp421.42MB

008.TensorFlow framework/015. What is TensorFlow.mp439.44MB

008.TensorFlow framework/016. Our first model in TensorFlow.mp436.8MB

009.Philosophy of deep learning/017. What Deep Learning is and is not.mp429.46MB

009.Philosophy of deep learning/018. Deep learning as a language.mp424.6MB

010.Introduction to CNN/019. Motivation for convolutional layers.mp441.38MB

010.Introduction to CNN/020. Our first CNN architecture.mp442.57MB

011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp457.9MB

011.Modern CNNs/022. Overview of modern CNN architectures.mp432.24MB

012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp419.28MB

012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp430.74MB

013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp423.78MB

013.Intro to Unsupervised Learning/026. Autoencoders 101.mp422.14MB

014.More Autoencoders/027. Autoencoder applications.mp440.85MB

014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp428.21MB

015.Word Embeddings/029. Natural language processing primer.mp436.68MB

015.Word Embeddings/030. Word embeddings.mp448.35MB

016.Generative Adversarial Networks/031. Generative models 101.mp426.68MB

016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp436.16MB

016.Generative Adversarial Networks/033. Applications of adversarial approach.mp441.89MB

017.Introduction to RNN/034. Motivation for recurrent layers.mp430.15MB

017.Introduction to RNN/035. Simple RNN and Backpropagation.mp435.07MB

018.Modern RNNs/036. The training of RNNs is not that easy.mp426.39MB

018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp434.86MB

018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp447.7MB

019.Applications of RNNs/039. Practical use cases for RNNs.mp456.12MB