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Title: UdemyGroup: NOGRPSource: Udemy
Info Hash
A1E9BB2A9609541DDD02A08E523362C7B41B510F
Source
Unverified
Total Size
2.85 GB
Total Files
100
Seeders
0
Leechers
1
Health
Score
1
Type
Bookware

File List

FileSize
1. Introduction and Outline/1. Introduction and Outline.mp43.27 MB
1. Introduction and Outline/1. Introduction and Outline.vtt351 B
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp45.19 MB
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.vtt351 B
1. Introduction and Outline/3. How to Succeed in this Course.mp46.41 MB
1. Introduction and Outline/3. How to Succeed in this Course.vtt351 B
1. Introduction and Outline/4. Where to get the code and data.mp426.43 MB
1. Introduction and Outline/4. Where to get the code and data.vtt351 B
1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.mp418.93 MB
1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.vtt351 B
1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.mp411.66 MB
1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.vtt351 B
10. Basics Review/1. (Review) Theano Basics.mp493.43 MB
10. Basics Review/1. (Review) Theano Basics.vtt6.31 KB
10. Basics Review/2. (Review) Theano Neural Network in Code.mp487.03 MB
10. Basics Review/2. (Review) Theano Neural Network in Code.vtt3.29 KB
10. Basics Review/3. (Review) Tensorflow Basics.mp481.47 MB
10. Basics Review/3. (Review) Tensorflow Basics.vtt5.06 KB
10. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp497.39 MB
10. Basics Review/4. (Review) Tensorflow Neural Network in Code.vtt4.78 KB
10. Basics Review/5. (Review) Keras Basics.mp427.64 MB
10. Basics Review/5. (Review) Keras Basics.vtt8.05 KB
10. Basics Review/6. (Review) Keras in Code pt 1.mp466.17 MB
10. Basics Review/6. (Review) Keras in Code pt 1.vtt6.47 KB
10. Basics Review/7. (Review) Keras in Code pt 2.mp438.67 MB
10. Basics Review/7. (Review) Keras in Code pt 2.vtt4.7 KB
11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.mp414.39 MB
11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.vtt10.39 KB
11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp49.37 MB
11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.vtt5.72 KB
11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.mp44.85 MB
11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.vtt3.01 KB
11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.mp410.88 MB
11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.vtt5.6 KB
12. Appendix/1. What is the Appendix.mp45.45 MB
12. Appendix/1. What is the Appendix.vtt3.28 KB
12. Appendix/10. Python 2 vs Python 3.mp47.84 MB
12. Appendix/10. Python 2 vs Python 3.vtt5.35 KB
12. Appendix/11. Is Theano Dead.mp417.82 MB
12. Appendix/11. Is Theano Dead.vtt11.3 KB
12. Appendix/12. What order should I take your courses in (part 1).mp429.33 MB
12. Appendix/12. What order should I take your courses in (part 1).vtt14.09 KB
12. Appendix/13. What order should I take your courses in (part 2).mp437.62 MB
12. Appendix/13. What order should I take your courses in (part 2).vtt20.24 KB
12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03 MB
12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt2.99 KB
12. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.39 MB
12. Appendix/3. Windows-Focused Environment Setup 2018.vtt17.39 KB
12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92 MB
12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt12.4 KB
12. Appendix/5. How to Code by Yourself (part 1).mp424.53 MB
12. Appendix/5. How to Code by Yourself (part 1).vtt19.78 KB
12. Appendix/6. How to Code by Yourself (part 2).mp414.8 MB
12. Appendix/6. How to Code by Yourself (part 2).vtt11.62 KB
12. Appendix/7. How to Succeed in this Course (Long Version).mp418.31 MB
12. Appendix/7. How to Succeed in this Course (Long Version).vtt12.79 KB
12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95 MB
12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt27.77 KB
12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.25 MB
12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt78.26 MB
2. Principal Components Analysis/1. What does PCA do.mp427.79 MB
2. Principal Components Analysis/1. What does PCA do.vtt4.96 KB
2. Principal Components Analysis/10. SVD (Singular Value Decomposition).mp442.47 MB
2. Principal Components Analysis/10. SVD (Singular Value Decomposition).vtt10.33 KB
2. Principal Components Analysis/2. How does PCA work.mp450.93 MB
2. Principal Components Analysis/2. How does PCA work.vtt12.37 KB
2. Principal Components Analysis/3. Why does PCA work (PCA derivation).mp451.32 MB
2. Principal Components Analysis/3. Why does PCA work (PCA derivation).vtt351 B
2. Principal Components Analysis/4. PCA only rotates.mp416.45 MB
2. Principal Components Analysis/4. PCA only rotates.vtt351 B
2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.mp49.39 MB
2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.vtt3.33 KB
2. Principal Components Analysis/6. PCA implementation.mp432.09 MB
2. Principal Components Analysis/6. PCA implementation.vtt351 B
2. Principal Components Analysis/7. PCA for NLP.mp416.62 MB
2. Principal Components Analysis/7. PCA for NLP.vtt3.89 KB
2. Principal Components Analysis/8. PCA objective function.mp43.68 MB
2. Principal Components Analysis/8. PCA objective function.vtt2.28 KB
2. Principal Components Analysis/9. PCA Application Naive Bayes.mp453.65 MB
2. Principal Components Analysis/9. PCA Application Naive Bayes.vtt10.78 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.mp47.9 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.vtt4.78 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.mp413.03 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.vtt4.82 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.mp415.1 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.vtt2.23 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.mp49.31 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.vtt3.64 KB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.mp44.35 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.vtt1.59 KB
4. Autoencoders/1. Autoencoders.mp45.82 MB
4. Autoencoders/1. Autoencoders.vtt3.94 KB
4. Autoencoders/10. Deep Autoencoder Visualization Description.mp42.46 MB
4. Autoencoders/10. Deep Autoencoder Visualization Description.vtt2 KB
4. Autoencoders/11. Deep Autoencoder Visualization in Code.mp427.85 MB
4. Autoencoders/11. Deep Autoencoder Visualization in Code.vtt6.67 KB
4. Autoencoders/12. An Autoencoder in 1 Line of Code.mp424.94 MB
4. Autoencoders/12. An Autoencoder in 1 Line of Code.vtt5.08 KB
4. Autoencoders/2. Denoising Autoencoders.mp43.44 MB
4. Autoencoders/2. Denoising Autoencoders.vtt2.26 KB

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