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fileunsupervised-deep-learni-1NJEY

unsupervised deep learning python
  • MP407 Extras Visualizing what features a neural network has learned\\/028 BONUS Where to get Udemy coupons and FREE deep learning terial.mp42.23MB
  • MP404 Autoencoders\\/019 Deep Autoencoder Visualization Desc<x>ription.mp42.45MB
  • MP401 Introduction and Outline\\/001 Introduction and Outline.mp43.27MB
  • MP404 Autoencoders\\/013 Desing Autoencoders.mp43.43MB
  • MP402 Principal Components Analysis\\/007 PCA ob<x>jective function.mp43.68MB
  • MP407 Extras Visualizing what features a neural network has learned\\/027 Exercises on feature visualization and interpretation.mp43.75MB
  • MP408 BONUS Application of PCA SVD to NLP Natural Language Processing\\/030 BONUS Application of PCA and SVD to NLP Natural Language Processing.mp43.93MB
  • MP403 t-SNE t-distributed Stochastic Neighbor em<x>bedding\\/011 t-SNE on MNIST.mp44.34MB
  • MP405 Restricted Boltznn chines\\/023 Contrastive Divergence for RBM Training.mp44.84MB
  • MP401 Introduction and Outline\\/002 Where does this course fit into your deep learning stus.mp45.18MB
  • MP406 The Vanishing Grant Problem\\/025 The Vanishing Grant Problem Desc<x>ription.mp45.20MB
  • MP404 Autoencoders\\/012 Autoencoders.mp45.82MB
  • MP404 Autoencoders\\/014 Stacked Autoencoders.mp46.60MB
  • MP402 Principal Components Analysis\\/005 PCA derivation.mp46.66MB
  • MP404 Autoencoders\\/018 Cross Entropy vs. KL Divergence.mp47.41MB
  • MP403 t-SNE t-distributed Stochastic Neighbor em<x>bedding\\/008 t-SNE Theory.mp47.90MB
  • MP403 t-SNE t-distributed Stochastic Neighbor em<x>bedding\\/010 t-SNE on XOR.mp49.31MB
  • MP405 Restricted Boltznn chines\\/022 Deriving Conditional Probabilities from Joint Probability.mp49.37MB
  • MP402 Principal Components Analysis\\/006 MNIST visualization finding the optil number of principal components.mp49.38MB
  • MP401 Introduction and Outline\\/003 How to Succeed in this Course.mp49.52MB
  • MP407 Extras Visualizing what features a neural network has learned\\/029 BONUS How to derive the free energy formula.mp410.88MB
  • MP402 Principal Components Analysis\\/004 What does PCA do.mp411.49MB
  • MP405 Restricted Boltznn chines\\/021 Restricted Boltznn Machine Theory.mp414.38MB
  • MP403 t-SNE t-distributed Stochastic Neighbor em<x>bedding\\/009 t-SNE on the Donut.mp415.10MB
  • MP404 Autoencoders\\/017 Testing greedy la<x>yer-wise autoencoder training vs. pure backpropagation.mp418.53MB
  • MP408 BONUS Application of PCA SVD to NLP Natural Language Processing\\/031 BONUS Latent Sentic Analysis in Code.mp425.61MB
  • MP408 BONUS Application of PCA SVD to NLP Natural Language Processing\\/032 BONUS Application of t-SNE K-Means Finding Clusters of Related Words.mp425.98MB
  • MP404 Autoencoders\\/020 Deep Autoencoder Visualization in Code.mp427.85MB
  • MP406 The Vanishing Grant Problem\\/026 The Vanishing Grant Problem Demo in Code.mp431.29MB
  • MP404 Autoencoders\\/015 Writing the autoencoder class in code.mp438.51MB
  • MP404 Autoencoders\\/016 Writing the deep neural network class in code.mp441.96MB
  • MP409 Appendix\\/033 How to install Numpy Scipy tplotlib Pandas IPython Theano and TensorFlow.mp443.92MB
  • MP405 Restricted Boltznn chines\\/024 RBM in Code Testing a greedily pre-trained deep belief network on MNIST.mp447.76MB
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