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Unsupervised S01E03 Field of Dreams ... and Dogs.mp4 51.9 MB
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- 【影视】 Udemy - Unsupervised Deep Learning in Python - TUTSEM
- 收录时间:2018-08-02 文档个数:42 文档大小:583.4 MB 最近下载:2025-05-14 人气:2605 磁力链接
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01 Introduction and Outline/001 Introduction and Outline.mp4 3.4 MB
01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp4 5.4 MB
01 Introduction and Outline/003 How to Succeed in this Course.mp4 10.0 MB
02 Principal Components Analysis/004 What does PCA do.mp4 12.1 MB
02 Principal Components Analysis/005 PCA derivation.mp4 7.0 MB
02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp4 9.8 MB
02 Principal Components Analysis/007 PCA objective function.mp4 3.9 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp4 8.3 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/009 t-SNE on the Donut.mp4 15.8 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp4 9.8 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp4 4.6 MB
04 Autoencoders/012 Autoencoders.mp4 6.1 MB
04 Autoencoders/013 Denoising Autoencoders.mp4 3.6 MB
04 Autoencoders/014 Stacked Autoencoders.mp4 6.9 MB
04 Autoencoders/015 Writing the autoencoder class in code Theano.mp4 40.4 MB
04 Autoencoders/016 Testing our Autoencoder Theano.mp4 11.9 MB
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- 收录时间:2017-08-18 文档个数:40 文档大小:583.4 MB 最近下载:2025-02-09 人气:1563 磁力链接
01 Introduction and Outline/001 Introduction and Outline.mp4 3.4 MB
01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp4 5.4 MB
01 Introduction and Outline/003 How to Succeed in this Course.mp4 10.0 MB
02 Principal Components Analysis/004 What does PCA do.mp4 12.1 MB
02 Principal Components Analysis/005 PCA derivation.mp4 7.0 MB
02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp4 9.8 MB
02 Principal Components Analysis/007 PCA objective function.mp4 3.9 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp4 8.3 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/009 t-SNE on the Donut.mp4 15.8 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp4 9.8 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp4 4.6 MB
04 Autoencoders/012 Autoencoders.mp4 6.1 MB
04 Autoencoders/013 Denoising Autoencoders.mp4 3.6 MB
04 Autoencoders/014 Stacked Autoencoders.mp4 6.9 MB
04 Autoencoders/015 Writing the autoencoder class in code Theano.mp4 40.4 MB
04 Autoencoders/016 Testing our Autoencoder Theano.mp4 11.9 MB
04 Autoencoders/017 Writing the deep neural network class in code Theano.mp4 44.0 MB
04 Autoencoders/018 Autoencoder in Code Tensorflow.mp4 25.6 MB
04 Autoencoders/019 Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 19.4 MB
04 Autoencoders/020 Cross Entropy vs. KL Divergence.mp4 7.8 MB
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- 【压缩文件】 K-Means for Cluster Analysis and Unsupervised Learning
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K-Means for Cluster Analysis and Unsupervised Learning.zip 576.9 MB
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07 Extras Visualizing what features a neural network has learned/028 BONUS Where to get Udemy coupons and FREE deep learning material.mp4 2.3 MB
04 Autoencoders/019 Deep Autoencoder Visualization Description.mp4 2.6 MB
01 Introduction and Outline/001 Introduction and Outline.mp4 3.4 MB
04 Autoencoders/013 Denoising Autoencoders.mp4 3.6 MB
02 Principal Components Analysis/007 PCA objective function.mp4 3.9 MB
07 Extras Visualizing what features a neural network has learned/027 Exercises on feature visualization and interpretation.mp4 3.9 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 4.1 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp4 4.6 MB
05 Restricted Boltzmann Machines/023 Contrastive Divergence for RBM Training.mp4 5.1 MB
01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp4 5.4 MB
06 The Vanishing Gradient Problem/025 The Vanishing Gradient Problem Description.mp4 5.5 MB
04 Autoencoders/012 Autoencoders.mp4 6.1 MB
04 Autoencoders/014 Stacked Autoencoders.mp4 6.9 MB
02 Principal Components Analysis/005 PCA derivation.mp4 7.0 MB
04 Autoencoders/018 Cross Entropy vs. KL Divergence.mp4 7.8 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp4 8.3 MB
03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp4 9.8 MB
05 Restricted Boltzmann Machines/022 Deriving Conditional Probabilities from Joint Probability.mp4 9.8 MB
02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp4 9.8 MB
01 Introduction and Outline/003 How to Succeed in this Course.mp4 10.0 MB
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