Udemy - Unsupervised Deep Learning in Python (11.2018)

Creation Time	July 7, 2025, 6:06 a.m.
Last Access Time	July 7, 2025, 6:06 a.m.
File Size	2.9 GB
Keywords	11 Unsupervised Python - Deep Udemy 2018 Learning in
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Total Files	168
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12. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186.4 MB
9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.mp4 128.5 MB
9. Applications to Recommender Systems/5. AutoRec in Code.mp4 102.3 MB
10. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97.4 MB
10. Basics Review/1. (Review) Theano Basics.mp4 93.4 MB
10. Basics Review/2. (Review) Theano Neural Network in Code.mp4 87.0 MB
9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.vtt 83.0 MB
9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.mp4 82.9 MB
10. Basics Review/3. (Review) Tensorflow Basics.mp4 81.5 MB
12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 78.3 MB
12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.mp4 70.4 MB
9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.mp4 68.2 MB
10. Basics Review/6. (Review) Keras in Code pt 1.mp4 66.2 MB
2. Principal Components Analysis/9. PCA Application Naive Bayes.mp4 53.6 MB
2. Principal Components Analysis/3. Why does PCA work (PCA derivation).mp4 51.3 MB
2. Principal Components Analysis/2. How does PCA work.mp4 50.9 MB
5. Restricted Boltzmann Machines/6. Training an RBM (part 1).mp4 49.1 MB
9. Applications to Recommender Systems/4. AutoRec.mp4 48.9 MB
5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.mp4 47.8 MB
9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.mp4 47.6 MB
12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.9 MB
2. Principal Components Analysis/10. SVD (Singular Value Decomposition).mp4 42.5 MB
4. Autoencoders/6. Writing the deep neural network class in code (Theano).mp4 42.0 MB
9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.mp4 39.6 MB
5. Restricted Boltzmann Machines/2. Introduction to RBMs.mp4 39.4 MB
12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39.0 MB
10. Basics Review/7. (Review) Keras in Code pt 2.mp4 38.7 MB
4. Autoencoders/4. Writing the autoencoder class in code (Theano).mp4 38.5 MB
9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.mp4 38.2 MB
12. Appendix/13. What order should I take your courses in (part 2).mp4 37.6 MB
5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.mp4 34.0 MB
5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.mp4 33.0 MB
2. Principal Components Analysis/6. PCA implementation.mp4 32.1 MB
5. Restricted Boltzmann Machines/5. Neural Network Equations.mp4 31.7 MB
6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.mp4 31.3 MB
12. Appendix/12. What order should I take your courses in (part 1).mp4 29.3 MB
4. Autoencoders/11. Deep Autoencoder Visualization in Code.mp4 27.9 MB
2. Principal Components Analysis/1. What does PCA do.mp4 27.8 MB
10. Basics Review/5. (Review) Keras Basics.mp4 27.6 MB
5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.mp4 27.6 MB
5. Restricted Boltzmann Machines/7. Training an RBM (part 2).mp4 27.3 MB
1. Introduction and Outline/4. Where to get the code and data.mp4 26.4 MB
8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp4 26.0 MB
8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.mp4 25.6 MB
4. Autoencoders/12. An Autoencoder in 1 Line of Code.mp4 24.9 MB
12. Appendix/5. How to Code by Yourself (part 1).mp4 24.5 MB
4. Autoencoders/7. Autoencoder in Code (Tensorflow).mp4 24.5 MB
5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.mp4 23.6 MB
9. Applications to Recommender Systems/3. Data Preparation and Logistics.mp4 21.2 MB
1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.mp4 18.9 MB
4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 18.5 MB
12. Appendix/7. How to Succeed in this Course (Long Version).mp4 18.3 MB
12. Appendix/11. Is Theano Dead.mp4 17.8 MB
2. Principal Components Analysis/7. PCA for NLP.mp4 16.6 MB
2. Principal Components Analysis/4. PCA only rotates.mp4 16.4 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.mp4 15.1 MB
12. Appendix/6. How to Code by Yourself (part 2).mp4 14.8 MB
11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.mp4 14.4 MB
5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).mp4 13.7 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.mp4 13.0 MB
5. Restricted Boltzmann Machines/4. Intractability.mp4 12.9 MB
1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.mp4 11.7 MB
4. Autoencoders/5. Testing our Autoencoder (Theano).mp4 11.4 MB
11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.mp4 10.9 MB
2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.mp4 9.4 MB
11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp4 9.4 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.mp4 9.3 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.mp4 7.9 MB
12. Appendix/10. Python 2 vs Python 3.mp4 7.8 MB
4. Autoencoders/9. Cross Entropy vs. KL Divergence.mp4 7.4 MB
4. Autoencoders/3. Stacked Autoencoders.mp4 6.6 MB
1. Introduction and Outline/3. How to Succeed in this Course.mp4 6.4 MB
4. Autoencoders/1. Autoencoders.mp4 5.8 MB
12. Appendix/1. What is the Appendix.mp4 5.5 MB
6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.mp4 5.2 MB
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp4 5.2 MB
11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.mp4 4.8 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.mp4 4.4 MB
12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.0 MB
8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).mp4 3.9 MB
7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.mp4 3.8 MB
2. Principal Components Analysis/8. PCA objective function.mp4 3.7 MB
4. Autoencoders/2. Denoising Autoencoders.mp4 3.4 MB
1. Introduction and Outline/1. Introduction and Outline.mp4 3.3 MB
4. Autoencoders/10. Deep Autoencoder Visualization Description.mp4 2.5 MB
12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.8 KB
12. Appendix/13. What order should I take your courses in (part 2).vtt 20.2 KB
12. Appendix/5. How to Code by Yourself (part 1).vtt 19.8 KB
12. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17.4 KB
12. Appendix/12. What order should I take your courses in (part 1).vtt 14.1 KB
12. Appendix/7. How to Succeed in this Course (Long Version).vtt 12.8 KB
9. Applications to Recommender Systems/5. AutoRec in Code.vtt 12.6 KB
12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.4 KB
2. Principal Components Analysis/2. How does PCA work.vtt 12.4 KB
9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.vtt 12.0 KB
9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.vtt 12.0 KB
5. Restricted Boltzmann Machines/6. Training an RBM (part 1).vtt 11.8 KB
12. Appendix/6. How to Code by Yourself (part 2).vtt 11.6 KB
12. Appendix/11. Is Theano Dead.vtt 11.3 KB
2. Principal Components Analysis/9. PCA Application Naive Bayes.vtt 10.8 KB
11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.vtt 10.4 KB
2. Principal Components Analysis/10. SVD (Singular Value Decomposition).vtt 10.3 KB
9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.vtt 8.7 KB
4. Autoencoders/7. Autoencoder in Code (Tensorflow).vtt 8.2 KB
10. Basics Review/5. (Review) Keras Basics.vtt 8.0 KB
5. Restricted Boltzmann Machines/5. Neural Network Equations.vtt 7.4 KB
5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.vtt 7.0 KB
5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.vtt 6.8 KB
4. Autoencoders/11. Deep Autoencoder Visualization in Code.vtt 6.7 KB
10. Basics Review/6. (Review) Keras in Code pt 1.vtt 6.5 KB
5. Restricted Boltzmann Machines/7. Training an RBM (part 2).vtt 6.4 KB
4. Autoencoders/6. Writing the deep neural network class in code (Theano).vtt 6.4 KB
10. Basics Review/1. (Review) Theano Basics.vtt 6.3 KB
4. Autoencoders/4. Writing the autoencoder class in code (Theano).vtt 6.1 KB
11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.vtt 5.7 KB
5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.vtt 5.6 KB
11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.vtt 5.6 KB
4. Autoencoders/9. Cross Entropy vs. KL Divergence.vtt 5.5 KB
12. Appendix/10. Python 2 vs Python 3.vtt 5.4 KB
5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.vtt 5.2 KB
4. Autoencoders/12. An Autoencoder in 1 Line of Code.vtt 5.1 KB
10. Basics Review/3. (Review) Tensorflow Basics.vtt 5.1 KB
2. Principal Components Analysis/1. What does PCA do.vtt 5.0 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.vtt 4.8 KB
10. Basics Review/4. (Review) Tensorflow Neural Network in Code.vtt 4.8 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.vtt 4.8 KB
10. Basics Review/7. (Review) Keras in Code pt 2.vtt 4.7 KB
9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.vtt 4.6 KB
4. Autoencoders/3. Stacked Autoencoders.vtt 4.2 KB
4. Autoencoders/1. Autoencoders.vtt 3.9 KB
2. Principal Components Analysis/7. PCA for NLP.vtt 3.9 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.vtt 3.6 KB
2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.vtt 3.3 KB
10. Basics Review/2. (Review) Theano Neural Network in Code.vtt 3.3 KB
12. Appendix/1. What is the Appendix.vtt 3.3 KB
11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.vtt 3.0 KB
12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3.0 KB
4. Autoencoders/5. Testing our Autoencoder (Theano).vtt 2.7 KB
2. Principal Components Analysis/8. PCA objective function.vtt 2.3 KB
4. Autoencoders/2. Denoising Autoencoders.vtt 2.3 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.vtt 2.2 KB
4. Autoencoders/10. Deep Autoencoder Visualization Description.vtt 2.0 KB
4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.vtt 1.9 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.vtt 1.6 KB
1. Introduction and Outline/1. Introduction and Outline.vtt 351 bytes
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.vtt 351 bytes
1. Introduction and Outline/3. How to Succeed in this Course.vtt 351 bytes
1. Introduction and Outline/4. Where to get the code and data.vtt 351 bytes
1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.vtt 351 bytes
1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.vtt 351 bytes
2. Principal Components Analysis/3. Why does PCA work (PCA derivation).vtt 351 bytes
2. Principal Components Analysis/4. PCA only rotates.vtt 351 bytes
2. Principal Components Analysis/6. PCA implementation.vtt 351 bytes
5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).vtt 351 bytes
5. Restricted Boltzmann Machines/2. Introduction to RBMs.vtt 351 bytes
5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.vtt 351 bytes
5. Restricted Boltzmann Machines/4. Intractability.vtt 351 bytes
6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.vtt 351 bytes
6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.vtt 351 bytes
7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.vtt 351 bytes
8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).vtt 351 bytes
8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.vtt 351 bytes
8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.vtt 351 bytes
9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.vtt 351 bytes
9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.vtt 351 bytes
9. Applications to Recommender Systems/3. Data Preparation and Logistics.vtt 351 bytes
9. Applications to Recommender Systems/4. AutoRec.vtt 351 bytes

Udemy - Unsupervised Deep Learning in Python (11.2018)

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