Udemy - Unsupervised Deep Learning in Python - TUTSEM
File List
- 05 Restricted Boltzmann Machines/026 RBM in Code Theano with Greedy Layer-Wise Training on MNIST.mp4 47.8 MB
- 09 Appendix/036 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 43.9 MB
- 04 Autoencoders/017 Writing the deep neural network class in code Theano.mp4 42.0 MB
- 04 Autoencoders/015 Writing the autoencoder class in code Theano.mp4 38.5 MB
- 06 The Vanishing Gradient Problem/029 The Vanishing Gradient Problem Demo in Code.mp4 31.3 MB
- 04 Autoencoders/022 Deep Autoencoder Visualization in Code.mp4 27.9 MB
- 08 BONUS Application of PCA SVD to NLP Natural Language Processing/035 BONUS Application of t-SNE K-Means Finding Clusters of Related Words.mp4 26.0 MB
- 08 BONUS Application of PCA SVD to NLP Natural Language Processing/034 BONUS Latent Semantic Analysis in Code.mp4 25.6 MB
- 09 Appendix/037 How to Code by Yourself part 1.mp4 24.5 MB
- 04 Autoencoders/018 Autoencoder in Code Tensorflow.mp4 24.5 MB
- 04 Autoencoders/019 Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 18.5 MB
- 03 t-SNE t-distributed Stochastic Neighbor Embedding/009 t-SNE on the Donut.mp4 15.1 MB
- 09 Appendix/038 How to Code by Yourself part 2.mp4 14.8 MB
- 05 Restricted Boltzmann Machines/023 Restricted Boltzmann Machine Theory.mp4 14.4 MB
- 05 Restricted Boltzmann Machines/027 RBM in Code Tensorflow.mp4 13.7 MB
- 02 Principal Components Analysis/004 What does PCA do.mp4 11.5 MB
- 04 Autoencoders/016 Testing our Autoencoder Theano.mp4 11.4 MB
- 07 Extras Visualizing what features a neural network has learned/032 BONUS How to derive the free energy formula.mp4 10.9 MB
- 01 Introduction and Outline/003 How to Succeed in this Course.mp4 9.5 MB
- 02 Principal Components Analysis/006 MNIST visualization finding the optimal number of principal components.mp4 9.4 MB
- 05 Restricted Boltzmann Machines/024 Deriving Conditional Probabilities from Joint Probability.mp4 9.4 MB
- 03 t-SNE t-distributed Stochastic Neighbor Embedding/010 t-SNE on XOR.mp4 9.3 MB
- 03 t-SNE t-distributed Stochastic Neighbor Embedding/008 t-SNE Theory.mp4 7.9 MB
- 04 Autoencoders/020 Cross Entropy vs. KL Divergence.mp4 7.4 MB
- 02 Principal Components Analysis/005 PCA derivation.mp4 6.7 MB
- 04 Autoencoders/014 Stacked Autoencoders.mp4 6.6 MB
- 04 Autoencoders/012 Autoencoders.mp4 5.8 MB
- 06 The Vanishing Gradient Problem/028 The Vanishing Gradient Problem Description.mp4 5.2 MB
- 01 Introduction and Outline/002 Where does this course fit into your deep learning studies.mp4 5.2 MB
- 05 Restricted Boltzmann Machines/025 Contrastive Divergence for RBM Training.mp4 4.8 MB
- 03 t-SNE t-distributed Stochastic Neighbor Embedding/011 t-SNE on MNIST.mp4 4.3 MB
- 08 BONUS Application of PCA SVD to NLP Natural Language Processing/033 BONUS Application of PCA and SVD to NLP Natural Language Processing.mp4 3.9 MB
- 07 Extras Visualizing what features a neural network has learned/030 Exercises on feature visualization and interpretation.mp4 3.8 MB
- 02 Principal Components Analysis/007 PCA objective function.mp4 3.7 MB
- 04 Autoencoders/013 Denoising Autoencoders.mp4 3.4 MB
- 01 Introduction and Outline/001 Introduction and Outline.mp4 3.3 MB
- 04 Autoencoders/021 Deep Autoencoder Visualization Description.mp4 2.5 MB
- 07 Extras Visualizing what features a neural network has learned/031 BONUS Where to get Udemy coupons and FREE deep learning material.mp4 2.2 MB
- Tutsem.com.lnk 2.7 KB
- TUTSEM.COM.txt 317 bytes
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