[Coursera] Introduction to Deep Learning

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[Coursera] Introduction to Deep Learning

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[Coursera] Introduction to Deep Learning
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001. Welcome to AML specialization!.mp4
MP4
13.7 MB
001. Welcome to AML specialization!.srt
SRT
4.7 KB
002. Course intro.mp4
MP4
22.1 MB
002. Course intro.srt
SRT
8.8 KB
003. Linear regression.mp4
MP4
35.7 MB
003. Linear regression.srt
SRT
13.3 KB
004. Linear classification.mp4
MP4
42.7 MB
004. Linear classification.srt
SRT
16.4 KB
005. Gradient descent.mp4
MP4
19 MB
005. Gradient descent.srt
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7.4 KB
006. Overfitting problem and model validation.mp4
MP4
26.4 MB
006. Overfitting problem and model validation.srt
SRT
9.8 KB
007. Model regularization.mp4
MP4
19.9 MB
007. Model regularization.srt
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7.4 KB
008. Stochastic gradient descent.mp4
MP4
21.1 MB
008. Stochastic gradient descent.srt
SRT
7.8 KB
009. Gradient descent extensions.mp4
MP4
36.6 MB
009. Gradient descent extensions.srt
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13.4 KB
010. Multilayer perceptron (MLP).mp4
MP4
44.7 MB
010. Multilayer perceptron (MLP).srt
SRT
18.5 KB
011. Chain rule.mp4
MP4
26.6 MB
011. Chain rule.srt
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10 KB
012. Backpropagation.mp4
MP4
31.6 MB
012. Backpropagation.srt
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11.4 KB
013. Efficient MLP implementation.mp4
MP4
47.1 MB
013. Efficient MLP implementation.srt
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16.6 KB
014. Other matrix derivatives.mp4
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21.4 MB
014. Other matrix derivatives.srt
SRT
8.6 KB
015. What is TensorFlow.mp4
MP4
39.4 MB
015. What is TensorFlow.srt
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14.7 KB
016. Our first model in TensorFlow.mp4
MP4
36.8 MB
016. Our first model in TensorFlow.srt
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13.8 KB
017. What Deep Learning is and is not.mp4
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29.5 MB
017. What Deep Learning is and is not.srt
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13.9 KB
018. Deep learning as a language.mp4
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24.6 MB
018. Deep learning as a language.srt
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11.9 KB
019. Motivation for convolutional layers.mp4
MP4
41.4 MB
019. Motivation for convolutional layers.srt
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16 KB
020. Our first CNN architecture.mp4
MP4
42.6 MB
020. Our first CNN architecture.srt
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13.3 KB
021. Training tips and tricks for deep CNNs.mp4
MP4
57.9 MB
021. Training tips and tricks for deep CNNs.srt
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18.2 KB
022. Overview of modern CNN architectures.mp4
MP4
32.2 MB
022. Overview of modern CNN architectures.srt
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9.5 KB
023. Learning new tasks with pre-trained CNNs.mp4
MP4
19.3 MB
023. Learning new tasks with pre-trained CNNs.srt
SRT
6.8 KB
024. A glimpse of other Computer Vision tasks.mp4
MP4
30.7 MB
024. A glimpse of other Computer Vision tasks.srt
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10.8 KB
025. Unsupervised learning what it is and why bother.mp4
MP4
23.8 MB
025. Unsupervised learning what it is and why bother.srt
SRT
9.5 KB
026. Autoencoders 101.mp4
MP4
22.1 MB
026. Autoencoders 101.srt
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8.1 KB
027. Autoencoder applications.mp4
MP4
40.8 MB
027. Autoencoder applications.srt
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14.7 KB
028. Autoencoder applications image generation, data visualization & more.mp4
MP4
28.2 MB
028. Autoencoder applications image generation, data visualization & more.srt
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10.6 KB
029. Natural language processing primer.mp4
MP4
36.7 MB
029. Natural language processing primer.srt
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15.3 KB
030. Word embeddings.mp4
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48.3 MB
030. Word embeddings.srt
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20.2 KB
031. Generative models 101.mp4
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26.7 MB
031. Generative models 101.srt
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11.2 KB
032. Generative Adversarial Networks.mp4
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36.2 MB
032. Generative Adversarial Networks.srt
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15.3 KB
033. Applications of adversarial approach.mp4
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41.9 MB
033. Applications of adversarial approach.srt
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15.9 KB
034. Motivation for recurrent layers.mp4
MP4
30.2 MB
034. Motivation for recurrent layers.srt
SRT
10.6 KB
035. Simple RNN and Backpropagation.mp4
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35.1 MB
035. Simple RNN and Backpropagation.srt
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12.5 KB
036. The training of RNNs is not that easy.mp4
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26.4 MB
036. The training of RNNs is not that easy.srt
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10.4 KB
037. Dealing with vanishing and exploding gradients.mp4
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34.9 MB
037. Dealing with vanishing and exploding gradients.srt
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13.7 KB
038. Modern RNNs LSTM and GRU.mp4
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47.7 MB
038. Modern RNNs LSTM and GRU.srt
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17.2 KB
039. Practical use cases for RNNs.mp4
MP4
56.1 MB
039. Practical use cases for RNNs.srt
SRT
19.5 KB
[CourseClub.NET].url
URL
102.4 B
[FCS Forum].url
URL
102.4 B
[FreeCourseSite.com].url
URL
102.4 B

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