| 1. (Legacy) Restricted Boltzmann Machine Theory.mp4 | 14.4 MB | ||
| 1. (Legacy) Restricted Boltzmann Machine Theory.vtt | 10.4 KB | ||
| 1. (Review) Theano Basics.mp4 | 93.4 MB | ||
| 1. (Review) Theano Basics.vtt | 6.3 KB | ||
| 1. Application of PCA and SVD to NLP (Natural Language Processing).mp4 | 3.9 MB | ||
| 1. Application of PCA and SVD to NLP (Natural Language Processing).vtt | 307.2 B | ||
| 1. Autoencoders.mp4 | 5.8 MB | ||
| 1. Autoencoders.vtt | 3.9 KB | ||
| 1. Basic Outline for RBMs.mp4 | 33 MB | ||
| 1. Basic Outline for RBMs.vtt | 5.6 KB | ||
| 1. Exercises on feature visualization and interpretation.mp4 | 3.8 MB | ||
| 1. Exercises on feature visualization and interpretation.vtt | 307.2 B | ||
| 1. Introduction and Outline.mp4 | 3.3 MB | ||
| 1. Introduction and Outline.vtt | 307.2 B | ||
| 1. Recommender Systems Section Introduction.mp4 | 68.2 MB | ||
| 1. Recommender Systems Section Introduction.vtt | 307.2 B | ||
| 1. The Vanishing Gradient Problem Description.mp4 | 5.2 MB | ||
| 1. The Vanishing Gradient Problem Description.vtt | 307.2 B | ||
| 1. What does PCA do.mp4 | 27.8 MB | ||
| 1. What does PCA do.vtt | 5 KB | ||
| 1. What is the Appendix.mp4 | 5.5 MB | ||
| 1. What is the Appendix.vtt | 3.3 KB | ||
| 1. t-SNE Theory.mp4 | 7.9 MB | ||
| 1. t-SNE Theory.vtt | 4.8 KB | ||
| 10. Deep Autoencoder Visualization Description.mp4 | 2.5 MB | ||
| 10. Deep Autoencoder Visualization Description.vtt | 2 KB | ||
| 10. Python 2 vs Python 3.mp4 | 7.8 MB | ||
| 10. Python 2 vs Python 3.vtt | 5.4 KB | ||
| 10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.mp4 | 47.8 MB | ||
| 10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.vtt | 6.8 KB | ||
| 10. Recommender RBM Code Speedup.mp4 | 82.9 MB | ||
| 10. Recommender RBM Code Speedup.vtt | 83 MB | ||
| 10. SVD (Singular Value Decomposition).mp4 | 42.5 MB | ||
| 10. SVD (Singular Value Decomposition).vtt | 10.3 KB | ||
| 11. Deep Autoencoder Visualization in Code.mp4 | 27.9 MB | ||
| 11. Deep Autoencoder Visualization in Code.vtt | 6.7 KB | ||
| 11. Is Theano Dead.mp4 | 17.8 MB | ||
| 11. Is Theano Dead.vtt | 11.3 KB | ||
| 11. RBM in Code (Tensorflow).mp4 | 13.7 MB | ||
| 11. RBM in Code (Tensorflow).vtt | 307.2 B | ||
| 12. An Autoencoder in 1 Line of Code.mp4 | 24.9 MB | ||
| 12. An Autoencoder in 1 Line of Code.vtt | 5.1 KB | ||
| 12. What order should I take your courses in (part 1).mp4 | 29.3 MB | ||
| 12. What order should I take your courses in (part 1).vtt | 14.1 KB | ||
| 13. What order should I take your courses in (part 2).mp4 | 37.6 MB | ||
| 13. What order should I take your courses in (part 2).vtt | 20.2 KB | ||
| 2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp4 | 9.4 MB | ||
| 2. (Legacy) Deriving Conditional Probabilities from Joint Probability.vtt | 5.7 KB | ||
| 2. (Review) Theano Neural Network in Code.mp4 | 87 MB | ||
| 2. (Review) Theano Neural Network in Code.vtt | 3.3 KB | ||
| 2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 | 4 MB | ||
| 2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt | 3 KB | ||
| 2. Denoising Autoencoders.mp4 | 3.4 MB | ||
| 2. Denoising Autoencoders.vtt | 2.3 KB | ||
| 2. How does PCA work.mp4 | 50.9 MB | ||
| 2. How does PCA work.vtt | 12.4 KB | ||
| 2. Introduction to RBMs.mp4 | 39.4 MB | ||
| 2. Introduction to RBMs.vtt | 307.2 B | ||
| 2. Latent Semantic Analysis in Code.mp4 | 25.6 MB | ||
| 2. Latent Semantic Analysis in Code.vtt | 307.2 B | ||
| 2. The Vanishing Gradient Problem Demo in Code.mp4 | 31.3 MB | ||
| 2. The Vanishing Gradient Problem Demo in Code.vtt | 307.2 B | ||
| 2. Where does this course fit into your deep learning studies.mp4 | 5.2 MB | ||
| 2. Where does this course fit into your deep learning studies.vtt | 307.2 B | ||
| 2. Why Autoencoders and RBMs work.mp4 | 38.2 MB | ||
| 2. Why Autoencoders and RBMs work.vtt | 307.2 B | ||
| 2. t-SNE Visualization.mp4 | 13 MB | ||
| 2. t-SNE Visualization.vtt | 4.8 KB | ||
| 3. (Legacy) Contrastive Divergence for RBM Training.mp4 | 4.8 MB | ||
| 3. (Legacy) Contrastive Divergence for RBM Training.vtt | 3 KB | ||
| 3. (Review) Tensorflow Basics.mp4 | 81.5 MB | ||
| 3. (Review) Tensorflow Basics.vtt | 5.1 KB | ||
| 3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp4 | 26 MB | ||
| 3. Application of t-SNE + K-Means Finding Clusters of Related Words.vtt | 307.2 B | ||
| 3. Data Preparation and Logistics.mp4 | 21.2 MB | ||
| 3. Data Preparation and Logistics.vtt | 307.2 B | ||
| 3. How to Succeed in this Course.mp4 | 6.4 MB | ||
| 3. How to Succeed in this Course.vtt | 307.2 B | ||
| 3. Motivation Behind RBMs.mp4 | 34 MB | ||
| 3. Motivation Behind RBMs.vtt | 307.2 B | ||
| 3. Stacked Autoencoders.mp4 | 6.6 MB | ||
| 3. Stacked Autoencoders.vtt | 4.2 KB | ||
| 3. Why does PCA work (PCA derivation).mp4 | 51.3 MB | ||
| 3. Why does PCA work (PCA derivation).vtt | 307.2 B | ||
| 3. Windows-Focused Environment Setup 2018.mp4 | 186.4 MB | ||
| 3. Windows-Focused Environment Setup 2018.vtt | 17.4 KB | ||
| 3. t-SNE on the Donut.mp4 | 15.1 MB | ||
| 3. t-SNE on the Donut.vtt | 2.2 KB | ||
| 4. (Legacy) How to derive the free energy formula.mp4 | 10.9 MB | ||
| 4. (Legacy) How to derive the free energy formula.vtt | 5.6 KB | ||
| 4. (Review) Tensorflow Neural Network in Code.mp4 | 97.4 MB | ||
| 4. (Review) Tensorflow Neural Network in Code.vtt | 4.8 KB | ||
| 4. AutoRec.mp4 | 48.9 MB | ||
| 4. AutoRec.vtt | 307.2 B | ||
| 4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 43.9 MB | ||
| 4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt | 12.4 KB | ||
| 4. Intractability.mp4 | 12.9 MB | ||
| 4. Intractability.vtt | 307.2 B | ||
| 4. PCA only rotates.mp4 | 16.4 MB | ||
| 4. PCA only rotates.vtt | 307.2 B | ||
| 4. Where to get the code and data.mp4 | 26.4 MB | ||
| 4. Where to get the code and data.vtt | 307.2 B | ||
| 4. Writing the autoencoder class in code (Theano).mp4 | 38.5 MB | ||
| 4. Writing the autoencoder class in code (Theano).vtt | 6.1 KB | ||
| 4. t-SNE on XOR.mp4 | 9.3 MB | ||
| 4. t-SNE on XOR.vtt | 3.6 KB | ||
| 5. (Review) Keras Basics.mp4 | 27.6 MB | ||
| 5. (Review) Keras Basics.vtt | 8 KB | ||
| 5. AutoRec in Code.mp4 | 102.3 MB | ||
| 5. AutoRec in Code.vtt | 12.6 KB | ||
| 5. How to Code by Yourself (part 1).mp4 | 24.5 MB | ||
| 5. How to Code by Yourself (part 1).vtt | 19.8 KB | ||
| 5. MNIST visualization, finding the optimal number of principal components.mp4 | 9.4 MB | ||
| 5. MNIST visualization, finding the optimal number of principal components.vtt | 3.3 KB | ||
| 5. Neural Network Equations.mp4 | 31.7 MB | ||
| 5. Neural Network Equations.vtt | 7.4 KB | ||
| 5. Tensorflow or Theano - Your Choice!.mp4 | 18.9 MB | ||
| 5. Tensorflow or Theano - Your Choice!.vtt | 307.2 B | ||
| 5. Testing our Autoencoder (Theano).mp4 | 11.4 MB | ||
| 5. Testing our Autoencoder (Theano).vtt | 2.7 KB | ||
| 5. t-SNE on MNIST.mp4 | 4.4 MB | ||
| 5. t-SNE on MNIST.vtt | 1.6 KB | ||
| 6. (Review) Keras in Code pt 1.mp4 | 66.2 MB | ||
| 6. (Review) Keras in Code pt 1.vtt | 6.5 KB | ||
| 6. Categorical RBM for Recommender System Ratings.mp4 | 47.6 MB | ||
| 6. Categorical RBM for Recommender System Ratings.vtt | 12 KB | ||
| 6. How to Code by Yourself (part 2).mp4 | 14.8 MB | ||
| 6. How to Code by Yourself (part 2).vtt | 11.6 KB | ||
| 6. PCA implementation.mp4 | 32.1 MB | ||
| 6. PCA implementation.vtt | 307.2 B | ||
| 6. Training an RBM (part 1).mp4 | 49.1 MB | ||
| 6. Training an RBM (part 1).vtt | 11.8 KB | ||
| 6. What are the practical applications of unsupervised deep learning.mp4 | 11.7 MB | ||
| 6. What are the practical applications of unsupervised deep learning.vtt | 307.2 B | ||
| 6. Writing the deep neural network class in code (Theano).mp4 | 42 MB | ||
| 6. Writing the deep neural network class in code (Theano).vtt | 6.4 KB | ||
| 7. (Review) Keras in Code pt 2.mp4 | 38.7 MB | ||
| 7. (Review) Keras in Code pt 2.vtt | 4.7 KB | ||
| 7. Autoencoder in Code (Tensorflow).mp4 | 24.5 MB | ||
| 7. Autoencoder in Code (Tensorflow).vtt | 8.2 KB | ||
| 7. How to Succeed in this Course (Long Version).mp4 | 18.3 MB | ||
| 7. How to Succeed in this Course (Long Version).vtt | 12.8 KB | ||
| 7. PCA for NLP.mp4 | 16.6 MB | ||
| 7. PCA for NLP.vtt | 3.9 KB | ||
| 7. Recommender RBM Code pt 1.mp4 | 70.4 MB | ||
| 7. Recommender RBM Code pt 1.vtt | 8.7 KB | ||
| 7. Training an RBM (part 2).mp4 | 27.3 MB | ||
| 7. Training an RBM (part 2).vtt | 6.4 KB | ||
| 8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 39 MB | ||
| 8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt | 27.8 KB | ||
| 8. PCA objective function.mp4 | 3.7 MB | ||
| 8. PCA objective function.vtt | 2.3 KB | ||
| 8. Recommender RBM Code pt 2.mp4 | 39.6 MB | ||
| 8. Recommender RBM Code pt 2.vtt | 4.6 KB | ||
| 8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 | 18.5 MB | ||
| 8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.vtt | 1.9 KB | ||
| 8. Training an RBM (part 3) - Free Energy.mp4 | 27.6 MB | ||
| 8. Training an RBM (part 3) - Free Energy.vtt | 7 KB | ||
| 9. Cross Entropy vs. KL Divergence.mp4 | 7.4 MB | ||
| 9. Cross Entropy vs. KL Divergence.vtt | 5.5 KB | ||
| 9. PCA Application Naive Bayes.mp4 | 53.6 MB | ||
| 9. PCA Application Naive Bayes.vtt | 10.8 KB | ||
| 9. Proof that using Jupyter Notebook is the same as not using it.mp4 | 78.3 MB | ||
| 9. Proof that using Jupyter Notebook is the same as not using it.vtt | 78.3 MB | ||
| 9. RBM Greedy Layer-Wise Pretraining.mp4 | 23.6 MB | ||
| 9. RBM Greedy Layer-Wise Pretraining.vtt | 5.2 KB | ||
| 9. Recommender RBM Code pt 3.mp4 | 128.5 MB | ||
| 9. Recommender RBM Code pt 3.vtt | 12 KB | ||
| [DesireCourse.Com].url | 0 B | ||
| ▲ 169 total files | |||
Unsupervised Deep Learning In Python
Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
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