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unsupervised-deep-learning-in-python
magnet:?xt=urn:btih:4da5482f6990bddd9b012015af648edc83274d68&dn=unsupervised-deep-learning-in-python
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Hash值:
4da5482f6990bddd9b012015af648edc83274d68
点击数:
98
文件大小:
467.52 MB
文件数量:
33
创建日期:
2018-2-28 22:23
最后访问:
2024-9-2 11:23
访问标签:
unsupervised-deep-learning-in-python
文件列表详情
07 Extras Visualizing what features a neural network has learned/028 BONUS Where to get Udemy coupons and FREE deep learning material.mp4 2.23 MB
04 Autoencoders/019 Deep Autoencoder Visualization Description.mp4 2.45 MB
01 Introduction and Outline/001 Introduction and Outline.mp4 3.27 MB
04 Autoencoders/013 Denoising Autoencoders.mp4 3.43 MB
02 Principal Components Analysis/007 PCA objective function.mp4 3.68 MB
07 Extras Visualizing what features a neural network has learned/027 Exercises on feature visualization and interpretation.mp4 3.75 MB
08 BONUS Application of PCA SVD to NLP Natural Language Processing/030 BONUS Application of PCA and SVD to NLP Natural Language Processing.mp4 3.93 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp4 4.34 MB
05 Restricted Boltzmann Machines/023 Contrastive Divergence for RBM Training.mp4 4.84 MB
01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp4 5.18 MB
06 The Vanishing Gradient Problem/025 The Vanishing Gradient Problem Description.mp4 5.2 MB
04 Autoencoders/012 Autoencoders.mp4 5.82 MB
04 Autoencoders/014 Stacked Autoencoders.mp4 6.6 MB
02 Principal Components Analysis/005 PCA derivation.mp4 6.66 MB
04 Autoencoders/018 Cross Entropy vs. KL Divergence.mp4 7.41 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp4 7.9 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp4 9.31 MB
05 Restricted Boltzmann Machines/022 Deriving Conditional Probabilities from Joint Probability.mp4 9.37 MB
02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp4 9.38 MB
01 Introduction and Outline/003 How to Succeed in this Course.mp4 9.52 MB
07 Extras Visualizing what features a neural network has learned/029 BONUS How to derive the free energy formula.mp4 10.88 MB
02 Principal Components Analysis/004 What does PCA do.mp4 11.49 MB
05 Restricted Boltzmann Machines/021 Restricted Boltzmann Machine Theory.mp4 14.38 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/009 t-SNE on the Donut.mp4 15.1 MB
04 Autoencoders/017 Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 18.53 MB
08 BONUS Application of PCA SVD to NLP Natural Language Processing/031 BONUS Latent Semantic Analysis in Code.mp4 25.61 MB
08 BONUS Application of PCA SVD to NLP Natural Language Processing/032 BONUS Application of t-SNE K-Means Finding Clusters of Related Words.mp4 25.98 MB
04 Autoencoders/020 Deep Autoencoder Visualization in Code.mp4 27.85 MB
06 The Vanishing Gradient Problem/026 The Vanishing Gradient Problem Demo in Code.mp4 31.29 MB
04 Autoencoders/015 Writing the autoencoder class in code.mp4 38.51 MB
04 Autoencoders/016 Writing the deep neural network class in code.mp4 41.96 MB
09 Appendix/033 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 43.92 MB
05 Restricted Boltzmann Machines/024 RBM in Code Testing a greedily pre-trained deep belief network on MNIST.mp4 47.76 MB
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