[CourseClub.NET] Coursera - Introduction to Deep Learning - 磁力狗

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文档列表

011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4 60.7 MB
019.Applications of RNNs/039. Practical use cases for RNNs.mp4 58.8 MB
015.Word Embeddings/030. Word embeddings.mp4 50.7 MB
018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4 50.0 MB
007.Matrix derivatives/013. Efficient MLP implementation.mp4 49.4 MB
006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4 46.8 MB
003.Linear model as the simplest neural network/004. Linear classification.mp4 44.7 MB
010.Introduction to CNN/020. Our first CNN architecture.mp4 44.6 MB
016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4 43.9 MB
010.Introduction to CNN/019. Motivation for convolutional layers.mp4 43.4 MB
014.More Autoencoders/027. Autoencoder applications.mp4 42.8 MB
008.TensorFlow framework/015. What is TensorFlow.mp4 41.4 MB
008.TensorFlow framework/016. Our first model in TensorFlow.mp4 38.6 MB
015.Word Embeddings/029. Natural language processing primer.mp4 38.5 MB
005.Stochastic methods for optimization/009. Gradient descent extensions.mp4 38.3 MB
016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4 37.9 MB
003.Linear model as the simplest neural network/003. Linear regression.mp4 37.5 MB
017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4 36.8 MB
018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4 36.6 MB
011.Modern CNNs/022. Overview of modern CNN architectures.mp4 33.8 MB
006.The simplest neural network MLP/012. Backpropagation.mp4 33.2 MB
012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4 32.2 MB
017.Introduction to RNN/034. Motivation for recurrent layers.mp4 31.6 MB
009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4 30.9 MB
014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4 29.6 MB
016.Generative Adversarial Networks/031. Generative models 101.mp4 28.0 MB
006.The simplest neural network MLP/011. Chain rule.mp4 27.9 MB
004.Regularization in machine learning/006. Overfitting problem and model validation.mp4 27.7 MB
018.Modern RNNs/036. The training of RNNs is not that easy.mp4 27.7 MB
009.Philosophy of deep learning/018. Deep learning as a language.mp4 25.8 MB
013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4 24.9 MB
013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4 23.2 MB
002.Course intro/002. Course intro.mp4 23.2 MB
007.Matrix derivatives/014. Other matrix derivatives.mp4 22.5 MB
005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4 22.1 MB
004.Regularization in machine learning/007. Model regularization.mp4 20.8 MB
012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4 20.2 MB
003.Linear model as the simplest neural network/005. Gradient descent.mp4 19.9 MB
001.Specialization Promo/001. Welcome to AML specialization!.mp4 14.3 MB
015.Word Embeddings/030. Word embeddings.srt 20.7 kB
019.Applications of RNNs/039. Practical use cases for RNNs.srt 19.9 kB
006.The simplest neural network MLP/010. Multilayer perceptron (MLP).srt 19.0 kB
011.Modern CNNs/021. Training tips and tricks for deep CNNs.srt 18.6 kB
018.Modern RNNs/038. Modern RNNs LSTM and GRU.srt 17.6 kB
007.Matrix derivatives/013. Efficient MLP implementation.srt 17.0 kB
003.Linear model as the simplest neural network/004. Linear classification.srt 16.8 kB
010.Introduction to CNN/019. Motivation for convolutional layers.srt 16.4 kB
016.Generative Adversarial Networks/033. Applications of adversarial approach.srt 16.3 kB
016.Generative Adversarial Networks/032. Generative Adversarial Networks.srt 15.7 kB
015.Word Embeddings/029. Natural language processing primer.srt 15.7 kB
014.More Autoencoders/027. Autoencoder applications.srt 15.1 kB
008.TensorFlow framework/015. What is TensorFlow.srt 15.0 kB
009.Philosophy of deep learning/017. What Deep Learning is and is not.srt 14.2 kB
008.TensorFlow framework/016. Our first model in TensorFlow.srt 14.2 kB
018.Modern RNNs/037. Dealing with vanishing and exploding gradients.srt 14.0 kB
005.Stochastic methods for optimization/009. Gradient descent extensions.srt 13.7 kB
003.Linear model as the simplest neural network/003. Linear regression.srt 13.7 kB
010.Introduction to CNN/020. Our first CNN architecture.srt 13.6 kB
017.Introduction to RNN/035. Simple RNN and Backpropagation.srt 12.8 kB
009.Philosophy of deep learning/018. Deep learning as a language.srt 12.2 kB
006.The simplest neural network MLP/012. Backpropagation.srt 11.6 kB
016.Generative Adversarial Networks/031. Generative models 101.srt 11.5 kB
012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.srt 11.0 kB
014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.srt 10.9 kB
017.Introduction to RNN/034. Motivation for recurrent layers.srt 10.8 kB
018.Modern RNNs/036. The training of RNNs is not that easy.srt 10.6 kB
006.The simplest neural network MLP/011. Chain rule.srt 10.2 kB
004.Regularization in machine learning/006. Overfitting problem and model validation.srt 10.0 kB
013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.srt 9.8 kB
011.Modern CNNs/022. Overview of modern CNN architectures.srt 9.7 kB
002.Course intro/002. Course intro.srt 9.0 kB
007.Matrix derivatives/014. Other matrix derivatives.srt 8.8 kB
013.Intro to Unsupervised Learning/026. Autoencoders 101.srt 8.3 kB
005.Stochastic methods for optimization/008. Stochastic gradient descent.srt 8.0 kB
004.Regularization in machine learning/007. Model regularization.srt 7.6 kB
003.Linear model as the simplest neural network/005. Gradient descent.srt 7.6 kB
012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.srt 7.0 kB
001.Specialization Promo/001. Welcome to AML specialization!.srt 4.8 kB
[FCS Forum].url 133 Bytes
[FreeCourseSite.com].url 127 Bytes
[CourseClub.NET].url 123 Bytes

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