种子简介
种子名称：
unsupervised-deep-learning-in-python
文件类型：
视频
文件数目：
33个文件
文件大小：
467.52 MB
收录时间：
2018-2-28 22:23
已经下载：
3次
资源热度：
95
最近下载：
2024-5-6 00:59
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种子包含的文件
unsupervised-deep-learning-in-python.torrent
07 Extras Visualizing what features a neural network has learned/028 BONUS Where to get Udemy coupons and FREE deep learning material.mp42.23MB
04 Autoencoders/019 Deep Autoencoder Visualization Description.mp42.45MB
01 Introduction and Outline/001 Introduction and Outline.mp43.27MB
04 Autoencoders/013 Denoising Autoencoders.mp43.43MB
02 Principal Components Analysis/007 PCA objective function.mp43.68MB
07 Extras Visualizing what features a neural network has learned/027 Exercises on feature visualization and interpretation.mp43.75MB
08 BONUS Application of PCA SVD to NLP Natural Language Processing/030 BONUS Application of PCA and SVD to NLP Natural Language Processing.mp43.93MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp44.34MB
05 Restricted Boltzmann Machines/023 Contrastive Divergence for RBM Training.mp44.84MB
01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp45.18MB
06 The Vanishing Gradient Problem/025 The Vanishing Gradient Problem Description.mp45.2MB
04 Autoencoders/012 Autoencoders.mp45.82MB
04 Autoencoders/014 Stacked Autoencoders.mp46.6MB
02 Principal Components Analysis/005 PCA derivation.mp46.66MB
04 Autoencoders/018 Cross Entropy vs. KL Divergence.mp47.41MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp47.9MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp49.31MB
05 Restricted Boltzmann Machines/022 Deriving Conditional Probabilities from Joint Probability.mp49.37MB
02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp49.38MB
01 Introduction and Outline/003 How to Succeed in this Course.mp49.52MB
07 Extras Visualizing what features a neural network has learned/029 BONUS How to derive the free energy formula.mp410.88MB
02 Principal Components Analysis/004 What does PCA do.mp411.49MB
05 Restricted Boltzmann Machines/021 Restricted Boltzmann Machine Theory.mp414.38MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/009 t-SNE on the Donut.mp415.1MB
04 Autoencoders/017 Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp418.53MB
08 BONUS Application of PCA SVD to NLP Natural Language Processing/031 BONUS Latent Semantic Analysis in Code.mp425.61MB
08 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
04 Autoencoders/020 Deep Autoencoder Visualization in Code.mp427.85MB
06 The Vanishing Gradient Problem/026 The Vanishing Gradient Problem Demo in Code.mp431.29MB
04 Autoencoders/015 Writing the autoencoder class in code.mp438.51MB
04 Autoencoders/016 Writing the deep neural network class in code.mp441.96MB
09 Appendix/033 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp443.92MB
05 Restricted Boltzmann Machines/024 RBM in Code Testing a greedily pre-trained deep belief network on MNIST.mp447.76MB
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