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[DesireCourse.Com] Udemy - Unsupervised Deep Learning in Python

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视频 2019-4-20 16:41 2024-10-21 04:03 118 2.7 GB 84
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DesireCourse Com Udemy - Unsupervised Deep Learning in Python

文件列表
  1. 1. Introduction and Outline/1. Introduction and Outline.mp43.27MB
  2. 1. Introduction and Outline/2. Where does this course fit into your deep learning studies.mp45.19MB
  3. 1. Introduction and Outline/3. How to Succeed in this Course.mp46.41MB
  4. 1. Introduction and Outline/4. Where to get the code and data.mp426.43MB
  5. 1. Introduction and Outline/5. Tensorflow or Theano - Your Choice!.mp418.93MB
  6. 1. Introduction and Outline/6. What are the practical applications of unsupervised deep learning.mp411.66MB
  7. 10. Basics Review/1. (Review) Theano Basics.mp493.43MB
  8. 10. Basics Review/2. (Review) Theano Neural Network in Code.mp487.03MB
  9. 10. Basics Review/3. (Review) Tensorflow Basics.mp481.47MB
  10. 10. Basics Review/4. (Review) Tensorflow Neural Network in Code.mp497.39MB
  11. 10. Basics Review/5. (Review) Keras Basics.mp427.64MB
  12. 10. Basics Review/6. (Review) Keras in Code pt 1.mp466.17MB
  13. 10. Basics Review/7. (Review) Keras in Code pt 2.mp438.67MB
  14. 11. Optional - Legacy RBM Lectures/1. (Legacy) Restricted Boltzmann Machine Theory.mp414.39MB
  15. 11. Optional - Legacy RBM Lectures/2. (Legacy) Deriving Conditional Probabilities from Joint Probability.mp49.37MB
  16. 11. Optional - Legacy RBM Lectures/3. (Legacy) Contrastive Divergence for RBM Training.mp44.85MB
  17. 11. Optional - Legacy RBM Lectures/4. (Legacy) How to derive the free energy formula.mp410.88MB
  18. 12. Appendix/1. What is the Appendix.mp45.45MB
  19. 12. Appendix/10. Python 2 vs Python 3.mp47.84MB
  20. 12. Appendix/11. Is Theano Dead.mp417.82MB
  21. 12. Appendix/12. What order should I take your courses in (part 1).mp429.33MB
  22. 12. Appendix/13. What order should I take your courses in (part 2).mp437.62MB
  23. 12. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp44.03MB
  24. 12. Appendix/3. Windows-Focused Environment Setup 2018.mp4186.39MB
  25. 12. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp443.92MB
  26. 12. Appendix/5. How to Code by Yourself (part 1).mp424.53MB
  27. 12. Appendix/6. How to Code by Yourself (part 2).mp414.8MB
  28. 12. Appendix/7. How to Succeed in this Course (Long Version).mp418.31MB
  29. 12. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.95MB
  30. 12. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp478.25MB
  31. 2. Principal Components Analysis/1. What does PCA do.mp427.79MB
  32. 2. Principal Components Analysis/10. SVD (Singular Value Decomposition).mp442.47MB
  33. 2. Principal Components Analysis/2. How does PCA work.mp450.93MB
  34. 2. Principal Components Analysis/3. Why does PCA work (PCA derivation).mp451.32MB
  35. 2. Principal Components Analysis/4. PCA only rotates.mp416.45MB
  36. 2. Principal Components Analysis/5. MNIST visualization, finding the optimal number of principal components.mp49.39MB
  37. 2. Principal Components Analysis/6. PCA implementation.mp432.09MB
  38. 2. Principal Components Analysis/7. PCA for NLP.mp416.62MB
  39. 2. Principal Components Analysis/8. PCA objective function.mp43.68MB
  40. 2. Principal Components Analysis/9. PCA Application Naive Bayes.mp453.65MB
  41. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/1. t-SNE Theory.mp47.9MB
  42. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/2. t-SNE Visualization.mp413.03MB
  43. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/3. t-SNE on the Donut.mp415.1MB
  44. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/4. t-SNE on XOR.mp49.31MB
  45. 3. t-SNE (t-distributed Stochastic Neighbor Embedding)/5. t-SNE on MNIST.mp44.35MB
  46. 4. Autoencoders/1. Autoencoders.mp45.82MB
  47. 4. Autoencoders/10. Deep Autoencoder Visualization Description.mp42.46MB
  48. 4. Autoencoders/11. Deep Autoencoder Visualization in Code.mp427.85MB
  49. 4. Autoencoders/12. An Autoencoder in 1 Line of Code.mp424.94MB
  50. 4. Autoencoders/2. Denoising Autoencoders.mp43.44MB
  51. 4. Autoencoders/3. Stacked Autoencoders.mp46.6MB
  52. 4. Autoencoders/4. Writing the autoencoder class in code (Theano).mp438.52MB
  53. 4. Autoencoders/5. Testing our Autoencoder (Theano).mp411.36MB
  54. 4. Autoencoders/6. Writing the deep neural network class in code (Theano).mp441.97MB
  55. 4. Autoencoders/7. Autoencoder in Code (Tensorflow).mp424.45MB
  56. 4. Autoencoders/8. Testing greedy layer-wise autoencoder training vs. pure backpropagation.mp418.53MB
  57. 4. Autoencoders/9. Cross Entropy vs. KL Divergence.mp47.42MB
  58. 5. Restricted Boltzmann Machines/1. Basic Outline for RBMs.mp432.98MB
  59. 5. Restricted Boltzmann Machines/10. RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST.mp447.76MB
  60. 5. Restricted Boltzmann Machines/11. RBM in Code (Tensorflow).mp413.7MB
  61. 5. Restricted Boltzmann Machines/2. Introduction to RBMs.mp439.44MB
  62. 5. Restricted Boltzmann Machines/3. Motivation Behind RBMs.mp434MB
  63. 5. Restricted Boltzmann Machines/4. Intractability.mp412.92MB
  64. 5. Restricted Boltzmann Machines/5. Neural Network Equations.mp431.71MB
  65. 5. Restricted Boltzmann Machines/6. Training an RBM (part 1).mp449.08MB
  66. 5. Restricted Boltzmann Machines/7. Training an RBM (part 2).mp427.34MB
  67. 5. Restricted Boltzmann Machines/8. Training an RBM (part 3) - Free Energy.mp427.58MB
  68. 5. Restricted Boltzmann Machines/9. RBM Greedy Layer-Wise Pretraining.mp423.62MB
  69. 6. The Vanishing Gradient Problem/1. The Vanishing Gradient Problem Description.mp45.2MB
  70. 6. The Vanishing Gradient Problem/2. The Vanishing Gradient Problem Demo in Code.mp431.29MB
  71. 7. Extras + Visualizing what features a neural network has learned/1. Exercises on feature visualization and interpretation.mp43.75MB
  72. 8. Applications to NLP (Natural Language Processing)/1. Application of PCA and SVD to NLP (Natural Language Processing).mp43.93MB
  73. 8. Applications to NLP (Natural Language Processing)/2. Latent Semantic Analysis in Code.mp425.62MB
  74. 8. Applications to NLP (Natural Language Processing)/3. Application of t-SNE + K-Means Finding Clusters of Related Words.mp425.99MB
  75. 9. Applications to Recommender Systems/1. Recommender Systems Section Introduction.mp468.17MB
  76. 9. Applications to Recommender Systems/10. Recommender RBM Code Speedup.mp482.95MB
  77. 9. Applications to Recommender Systems/2. Why Autoencoders and RBMs work.mp438.19MB
  78. 9. Applications to Recommender Systems/3. Data Preparation and Logistics.mp421.21MB
  79. 9. Applications to Recommender Systems/4. AutoRec.mp448.9MB
  80. 9. Applications to Recommender Systems/5. AutoRec in Code.mp4102.28MB
  81. 9. Applications to Recommender Systems/6. Categorical RBM for Recommender System Ratings.mp447.59MB
  82. 9. Applications to Recommender Systems/7. Recommender RBM Code pt 1.mp470.42MB
  83. 9. Applications to Recommender Systems/8. Recommender RBM Code pt 2.mp439.58MB
  84. 9. Applications to Recommender Systems/9. Recommender RBM Code pt 3.mp4128.54MB
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