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[DesireCourse.Com] Udemy - Unsupervised Deep Learning in Python
magnet:?xt=urn:btih:fa01761607262fc54021026dce9fb9fa6657b662&dn=[DesireCourse.Com] Udemy - Unsupervised Deep Learning in Python
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文件列表详情
fa01761607262fc54021026dce9fb9fa6657b662
infohash:
84
文件数量
2.7 GB
文件大小
2019-4-20 16:41
创建日期
2025-1-15 03:33
最后访问
相关分词
DesireCourse
Com
Udemy
-
Unsupervised
Deep
Learning
in
Python
1. Introduction and Outline/1. Introduction and Outline.mp4 3.27 MB
1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp4 5.19 MB
1. Introduction and Outline/3. How to Succeed in this Course.mp4 6.41 MB
1. Introduction and Outline/4. Where to get the code and data.mp4 26.43 MB
1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.mp4 18.93 MB
1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.mp4 11.66 MB
10. Basics Review/1. (Review) Theano Basics.mp4 93.43 MB
10. Basics Review/2. (Review) Theano Neural Network in Code.mp4 87.03 MB
10. Basics Review/3. (Review) Tensorflow Basics.mp4 81.47 MB
10. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp4 97.39 MB
10. Basics Review/5. (Review) Keras Basics.mp4 27.64 MB
10. Basics Review/6. (Review) Keras in Code pt 1.mp4 66.17 MB
10. Basics Review/7. (Review) Keras in Code pt 2.mp4 38.67 MB
11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.mp4 14.39 MB
11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp4 9.37 MB
11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.mp4 4.85 MB
11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.mp4 10.88 MB
12. Appendix/1. What is the Appendix.mp4 5.45 MB
12. Appendix/10. Python 2 vs Python 3.mp4 7.84 MB
12. Appendix/11. Is Theano Dead.mp4 17.82 MB
12. Appendix/12. What order should I take your courses in (part 1).mp4 29.33 MB
12. Appendix/13. What order should I take your courses in (part 2).mp4 37.62 MB
12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4.03 MB
12. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186.39 MB
12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 43.92 MB
12. Appendix/5. How to Code by Yourself (part 1).mp4 24.53 MB
12. Appendix/6. How to Code by Yourself (part 2).mp4 14.8 MB
12. Appendix/7. How to Succeed in this Course (Long Version).mp4 18.31 MB
12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 38.95 MB
12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78.25 MB
2. Principal Components Analysis/1. What does PCA do.mp4 27.79 MB
2. Principal Components Analysis/10. SVD (Singular Value Decomposition).mp4 42.47 MB
2. Principal Components Analysis/2. How does PCA work.mp4 50.93 MB
2. Principal Components Analysis/3. Why does PCA work (PCA derivation).mp4 51.32 MB
2. Principal Components Analysis/4. PCA only rotates.mp4 16.45 MB
2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.mp4 9.39 MB
2. Principal Components Analysis/6. PCA implementation.mp4 32.09 MB
2. Principal Components Analysis/7. PCA for NLP.mp4 16.62 MB
2. Principal Components Analysis/8. PCA objective function.mp4 3.68 MB
2. Principal Components Analysis/9. PCA Application Naive Bayes.mp4 53.65 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.mp4 7.9 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.mp4 13.03 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.mp4 15.1 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.mp4 9.31 MB
3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.mp4 4.35 MB
4. Autoencoders/1. Autoencoders.mp4 5.82 MB
4. Autoencoders/10. Deep Autoencoder Visualization Description.mp4 2.46 MB
4. Autoencoders/11. Deep Autoencoder Visualization in Code.mp4 27.85 MB
4. Autoencoders/12. An Autoencoder in 1 Line of Code.mp4 24.94 MB
4. Autoencoders/2. Denoising Autoencoders.mp4 3.44 MB
4. Autoencoders/3. Stacked Autoencoders.mp4 6.6 MB
4. Autoencoders/4. Writing the autoencoder class in code (Theano).mp4 38.52 MB
4. Autoencoders/5. Testing our Autoencoder (Theano).mp4 11.36 MB
4. Autoencoders/6. Writing the deep neural network class in code (Theano).mp4 41.97 MB
4. Autoencoders/7. Autoencoder in Code (Tensorflow).mp4 24.45 MB
4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp4 18.53 MB
4. Autoencoders/9. Cross Entropy vs. KL Divergence.mp4 7.42 MB
5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.mp4 32.98 MB
5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.mp4 47.76 MB
5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).mp4 13.7 MB
5. Restricted Boltzmann Machines/2. Introduction to RBMs.mp4 39.44 MB
5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.mp4 34 MB
5. Restricted Boltzmann Machines/4. Intractability.mp4 12.92 MB
5. Restricted Boltzmann Machines/5. Neural Network Equations.mp4 31.71 MB
5. Restricted Boltzmann Machines/6. Training an RBM (part 1).mp4 49.08 MB
5. Restricted Boltzmann Machines/7. Training an RBM (part 2).mp4 27.34 MB
5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.mp4 27.58 MB
5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.mp4 23.62 MB
6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.mp4 5.2 MB
6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.mp4 31.29 MB
7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.mp4 3.75 MB
8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).mp4 3.93 MB
8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.mp4 25.62 MB
8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp4 25.99 MB
9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.mp4 68.17 MB
9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.mp4 82.95 MB
9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.mp4 38.19 MB
9. Applications to Recommender Systems/3. Data Preparation and Logistics.mp4 21.21 MB
9. Applications to Recommender Systems/4. AutoRec.mp4 48.9 MB
9. Applications to Recommender Systems/5. AutoRec in Code.mp4 102.28 MB
9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.mp4 47.59 MB
9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.mp4 70.42 MB
9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.mp4 39.58 MB
9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.mp4 128.54 MB
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