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Recommender System Applications with Deep Learning
Welcome
Introduction (3:09)
Special Offer! Get the VIP version of this course (1:14)
Outline of the course (4:39)
Where to get the code (5:05)
Where to get help (2:06)
Simple Recommendation Systems
Section Introduction and Outline (4:19)
Perspective for this Section (3:41)
Basic Intuitions (5:14)
Associations (4:43)
Hacker News - Will you be penalized for talking about the NSA? (7:28)
Reddit - Should censorship based on politics be allowed? (8:54)
Problems with Average Rating & Explore vs. Exploit (part 1) (10:58)
Problems with Average Rating & Explore vs. Exploit (part 2) (7:39)
Bayesian Approach part 1 (Optional) (11:07)
Bayesian Approach part 2 (Sampling and Ranking) (5:57)
Bayesian Approach part 3 (Gaussian) (8:23)
Bayesian Approach part 4 (Code) (12:01)
Demographics and Supervised Learning (7:22)
PageRank (part 1) (11:12)
PageRank (part 2) (11:55)
Evaluating a Ranking (4:39)
Section Conclusion (4:10)
Collaborative Filtering
Collaborative Filtering Section Introduction (11:38)
User-User Collaborative Filtering (13:51)
Collaborative Filtering Exercise Prep (10:21)
Data Preprocessing (15:26)
User-User Collaborative Filtering in Code (16:06)
Item-Item Collaborative Filtering (9:15)
Item-Item Collaborative Filtering in Code (7:07)
Collaborative Filtering Section Conclusion (5:34)
Matrix Factorization and Deep Learning
Matrix Factorization Section Introduction (4:08)
Matrix Factorization - First Steps (15:27)
Matrix Factorization - Training (8:56)
Matrix Factorization - Expanding Our Model (8:04)
Matrix Factorization - Regularization (6:18)
Matrix Factorization - Exercise Prompt (1:15)
Matrix Factorization in Code (6:17)
Matrix Factorization in Code - Vectorized (10:14)
SVD (Singular Value Decomposition) (7:48)
Probabilistic Matrix Factorization (6:06)
Bayesian Matrix Factorization (5:34)
Matrix Factorization in Keras (Discussion) (7:32)
Matrix Factorization in Keras (Code) (7:14)
Deep Neural Network (Discussion) (2:51)
Deep Neural Network (Code) (2:43)
Residual Learning (Discussion) (2:03)
Residual Learning (Code) (1:59)
Autoencoders (AutoRec) Discussion (10:14)
Autoencoders (AutoRec) Code (11:45)
Big Data Matrix Factorization with Spark Cluster on AWS / EC2
Big Data and Spark Section Introduction (7:16)
Setting up Spark in your Local Environment (7:36)
Matrix Factorization in Spark (10:28)
Spark Submit (6:26)
Setting up a Spark Cluster on AWS / EC2 (12:38)
Making Predictions in the Real World (2:46)
Bonus: TF-IDF
TF-IDF Theory (9:55)
TF-IDF Code (9:18)
Basics Review
Keras Discussion (6:48)
Keras Neural Network in Code (6:37)
Keras Functional API (4:26)
How to easily convert Keras into Tensorflow 2.0 code (1:49)
Tensorflow Basics (7:27)
Tensorflow Neural Network in Code (9:43)
Confidence Intervals (10:11)
Gaussian Conjugate Prior (5:41)
Appendix
What is the Appendix? (2:48)
Windows-Focused Environment Setup 2018 (20:20)
How to How to install Numpy, Theano, Tensorflow, etc... (17:30)
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:04)
How to Succeed in this Course (Long Version) (10:24)
How to Code by Yourself (part 1) (15:54)
How to Code by Yourself (part 2) (9:23)
Proof that using Jupyter Notebook is the same as not using it (12:29)
Python 2 vs Python 3 (4:38)
What order should I take your courses in? (part 1) (11:18)
What order should I take your courses in? (part 2) (16:07)
Where to get discount coupons and FREE deep learning material (5:31)
Bayesian Approach part 2 (Sampling and Ranking)
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