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[CourseClub.NET] Coursera - Introduction to Deep Learning
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2019-7-31 19:08
2024-8-25 03:09
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1.27 GB
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磁力链接
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迅雷链接
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CourseClub
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Coursera
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Introduction
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Deep
Learning
文件列表
001.Specialization Promo/001. Welcome to AML specialization!.mp4
13.67MB
002.Course intro/002. Course intro.mp4
22.1MB
003.Linear model as the simplest neural network/003. Linear regression.mp4
35.73MB
003.Linear model as the simplest neural network/004. Linear classification.mp4
42.66MB
003.Linear model as the simplest neural network/005. Gradient descent.mp4
18.96MB
004.Regularization in machine learning/006. Overfitting problem and model validation.mp4
26.42MB
004.Regularization in machine learning/007. Model regularization.mp4
19.85MB
005.Stochastic methods for optimization/008. Stochastic gradient descent.mp4
21.1MB
005.Stochastic methods for optimization/009. Gradient descent extensions.mp4
36.57MB
006.The simplest neural network MLP/010. Multilayer perceptron (MLP).mp4
44.68MB
006.The simplest neural network MLP/011. Chain rule.mp4
26.59MB
006.The simplest neural network MLP/012. Backpropagation.mp4
31.63MB
007.Matrix derivatives/013. Efficient MLP implementation.mp4
47.09MB
007.Matrix derivatives/014. Other matrix derivatives.mp4
21.42MB
008.TensorFlow framework/015. What is TensorFlow.mp4
39.44MB
008.TensorFlow framework/016. Our first model in TensorFlow.mp4
36.8MB
009.Philosophy of deep learning/017. What Deep Learning is and is not.mp4
29.46MB
009.Philosophy of deep learning/018. Deep learning as a language.mp4
24.6MB
010.Introduction to CNN/019. Motivation for convolutional layers.mp4
41.38MB
010.Introduction to CNN/020. Our first CNN architecture.mp4
42.57MB
011.Modern CNNs/021. Training tips and tricks for deep CNNs.mp4
57.9MB
011.Modern CNNs/022. Overview of modern CNN architectures.mp4
32.24MB
012.Applications of CNNs/023. Learning new tasks with pre-trained CNNs.mp4
19.28MB
012.Applications of CNNs/024. A glimpse of other Computer Vision tasks.mp4
30.74MB
013.Intro to Unsupervised Learning/025. Unsupervised learning what it is and why bother.mp4
23.78MB
013.Intro to Unsupervised Learning/026. Autoencoders 101.mp4
22.14MB
014.More Autoencoders/027. Autoencoder applications.mp4
40.85MB
014.More Autoencoders/028. Autoencoder applications image generation, data visualization & more.mp4
28.21MB
015.Word Embeddings/029. Natural language processing primer.mp4
36.68MB
015.Word Embeddings/030. Word embeddings.mp4
48.35MB
016.Generative Adversarial Networks/031. Generative models 101.mp4
26.68MB
016.Generative Adversarial Networks/032. Generative Adversarial Networks.mp4
36.16MB
016.Generative Adversarial Networks/033. Applications of adversarial approach.mp4
41.89MB
017.Introduction to RNN/034. Motivation for recurrent layers.mp4
30.15MB
017.Introduction to RNN/035. Simple RNN and Backpropagation.mp4
35.07MB
018.Modern RNNs/036. The training of RNNs is not that easy.mp4
26.39MB
018.Modern RNNs/037. Dealing with vanishing and exploding gradients.mp4
34.86MB
018.Modern RNNs/038. Modern RNNs LSTM and GRU.mp4
47.7MB
019.Applications of RNNs/039. Practical use cases for RNNs.mp4
56.12MB
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