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[Coursera] Introduction to Deep Learning

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[Coursera] Introduction to Deep Learning (Size: 1.3 GB)
  001. Welcome to AML specialization!.mp4 13.7 MB
  001. Welcome to AML specialization!.srt 4.7 KB
  002. Course intro.mp4 22.1 MB
  002. Course intro.srt 8.8 KB
  003. Linear regression.mp4 35.7 MB
  003. Linear regression.srt 13.3 KB
  004. Linear classification.mp4 42.7 MB
  004. Linear classification.srt 16.4 KB
  005. Gradient descent.mp4 19 MB
  005. Gradient descent.srt 7.4 KB
  006. Overfitting problem and model validation.mp4 26.4 MB
  006. Overfitting problem and model validation.srt 9.8 KB
  007. Model regularization.mp4 19.9 MB
  007. Model regularization.srt 7.4 KB
  008. Stochastic gradient descent.mp4 21.1 MB
  008. Stochastic gradient descent.srt 7.8 KB
  009. Gradient descent extensions.mp4 36.6 MB
  009. Gradient descent extensions.srt 13.4 KB
  010. Multilayer perceptron (MLP).mp4 44.7 MB
  010. Multilayer perceptron (MLP).srt 18.5 KB
  011. Chain rule.mp4 26.6 MB
  011. Chain rule.srt 10 KB
  012. Backpropagation.mp4 31.6 MB
  012. Backpropagation.srt 11.4 KB
  013. Efficient MLP implementation.mp4 47.1 MB
  013. Efficient MLP implementation.srt 16.6 KB
  014. Other matrix derivatives.mp4 21.4 MB
  014. Other matrix derivatives.srt 8.6 KB
  015. What is TensorFlow.mp4 39.4 MB
  015. What is TensorFlow.srt 14.7 KB
  016. Our first model in TensorFlow.mp4 36.8 MB
  016. Our first model in TensorFlow.srt 13.8 KB
  017. What Deep Learning is and is not.mp4 29.5 MB
  017. What Deep Learning is and is not.srt 13.9 KB
  018. Deep learning as a language.mp4 24.6 MB
  018. Deep learning as a language.srt 11.9 KB
  019. Motivation for convolutional layers.mp4 41.4 MB
  019. Motivation for convolutional layers.srt 16 KB
  020. Our first CNN architecture.mp4 42.6 MB
  020. Our first CNN architecture.srt 13.3 KB
  021. Training tips and tricks for deep CNNs.mp4 57.9 MB
  021. Training tips and tricks for deep CNNs.srt 18.2 KB
  022. Overview of modern CNN architectures.mp4 32.2 MB
  022. Overview of modern CNN architectures.srt 9.5 KB
  023. Learning new tasks with pre-trained CNNs.mp4 19.3 MB
  023. Learning new tasks with pre-trained CNNs.srt 6.8 KB
  024. A glimpse of other Computer Vision tasks.mp4 30.7 MB
  024. A glimpse of other Computer Vision tasks.srt 10.8 KB
  025. Unsupervised learning what it is and why bother.mp4 23.8 MB
  025. Unsupervised learning what it is and why bother.srt 9.5 KB
  026. Autoencoders 101.mp4 22.1 MB
  026. Autoencoders 101.srt 8.1 KB
  027. Autoencoder applications.mp4 40.8 MB
  027. Autoencoder applications.srt 14.7 KB
  028. Autoencoder applications image generation, data visualization & more.mp4 28.2 MB
  028. Autoencoder applications image generation, data visualization & more.srt 10.6 KB
  029. Natural language processing primer.mp4 36.7 MB
  029. Natural language processing primer.srt 15.3 KB
  030. Word embeddings.mp4 48.3 MB
  030. Word embeddings.srt 20.2 KB
  031. Generative models 101.mp4 26.7 MB
  031. Generative models 101.srt 11.2 KB
  032. Generative Adversarial Networks.mp4 36.2 MB
  032. Generative Adversarial Networks.srt 15.3 KB
  033. Applications of adversarial approach.mp4 41.9 MB
  033. Applications of adversarial approach.srt 15.9 KB
  034. Motivation for recurrent layers.mp4 30.2 MB
  034. Motivation for recurrent layers.srt 10.6 KB
  035. Simple RNN and Backpropagation.mp4 35.1 MB
  035. Simple RNN and Backpropagation.srt 12.5 KB
  036. The training of RNNs is not that easy.mp4 26.4 MB
  036. The training of RNNs is not that easy.srt 10.4 KB
  037. Dealing with vanishing and exploding gradients.mp4 34.9 MB
  037. Dealing with vanishing and exploding gradients.srt 13.7 KB
  038. Modern RNNs LSTM and GRU.mp4 47.7 MB
  038. Modern RNNs LSTM and GRU.srt 17.2 KB
  039. Practical use cases for RNNs.mp4 56.1 MB
  039. Practical use cases for RNNs.srt 19.5 KB
  [CourseClub.NET].url 102.4 B
  [FCS Forum].url 102.4 B
  [FreeCourseSite.com].url 102.4 B
  ▲ 81 total files

Description


[Coursera] Introduction to Deep Learning

The goal of this course is to give learners basic understanding of modern neural networks.

For More Courses: https://courseclub.net

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