Recommender Systems and Deep Learning in Python
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       Video Length : 13h30m0s
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       Tasks Number : 89
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       Students Enrolled : 1575
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       Medium Level
  • Curriculum
  • 1. Welcome
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      Introduction
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      Outline of the course
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      Where to get the code
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  • 2. Simple Recommendation Systems
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      Section Introduction and Outline
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      Perspective for this Section
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      Basic Intuitions
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      Associations
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    • videocam
      Hacker News - Will you be penalized for talking about the NSA?
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      Reddit - Should censorship based on politics be allowed?
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      Problems with Average Rating & Explore vs. Exploit (part 1)
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      Problems with Average Rating & Explore vs. Exploit (part 2)
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      Bayesian Approach part 1 (Optional)
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      Bayesian Approach part 2 (Sampling and Ranking)
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      Bayesian Approach part 3 (Gaussian)
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      Bayesian Approach part 4 (Code)
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      Demographics and Supervised Learning
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      PageRank (part 1)
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      PageRank (part 2)
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      Evaluating a Ranking
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      Section Conclusion
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  • 3. Collaborative Filtering
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      Collaborative Filtering Section Introduction
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      User-User Collaborative Filtering
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      Collaborative Filtering Exercise Prep
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      Data Preprocessing
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      User-User Collaborative Filtering in Code
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      Item-Item Collaborative Filtering
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      Item-Item Collaborative Filtering in Code
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      Collaborative Filtering Section Conclusion
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  • 4. Matrix Factorization and Deep Learning
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      Matrix Factorization Section Introduction
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      Matrix Factorization - First Steps
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      Matrix Factorization - Training
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      Matrix Factorization - Expanding Our Model
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      Matrix Factorization - Regularization
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      Matrix Factorization - Exercise Prompt
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      Matrix Factorization in Code
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      Matrix Factorization in Code - Vectorized
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      SVD (Singular Value Decomposition)
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      Probabilistic Matrix Factorization
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      Bayesian Matrix Factorization
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      Matrix Factorization in Keras (Discussion)
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      Matrix Factorization in Keras (Code)
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      Deep Neural Network (Discussion)
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      Deep Neural Network (Code)
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      Residual Learning (Discussion)
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      Residual Learning (Code)
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      Autoencoders (AutoRec) Discussion
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      Autoencoders (AutoRec) Code
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  • 5. Restricted Boltzmann Machines (RBMs) for Collaborative Filtering
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      RBMs for Collaborative Filtering Section Introduction
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      Intro to RBMs
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      Motivation Behind RBMs
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      Intractability
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      Neural Network Equations
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      Training an RBM (part 1)
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      Training an RBM (part 2)
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      Training an RBM (part 3) - Free Energy
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    • videocam
      Categorical RBM for Recommender System Ratings
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      RBM Code pt 1
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      RBM Code pt 2
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      RBM Code pt 3
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      Speeding up the RBM Code
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  • 6. Big Data Matrix Factorization with Spark Cluster on AWS / EC2
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      Big Data and Spark Section Introduction
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      Setting up Spark in your Local Environment
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      Matrix Factorization in Spark
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      Spark Submit
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      Setting up a Spark Cluster on AWS / EC2
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      Making Predictions in the Real World
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  • 7. Basics Review
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      (Review) Keras Discussion
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      (Review) Keras Neural Network in Code
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    • videocam
      (Review) Keras Functional API
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      (Review) Confidence Intervals
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      (Review) Gaussian Conjugate Prior
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  • 8. Appendix
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      What is the Appendix?
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      Windows-Focused Environment Setup 2018
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      How to How to install Numpy, Theano, Tensorflow, etc...
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    • videocam
      Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
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    • videocam
      How to Succeed in this Course (Long Version)
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      How to Code by Yourself (part 1)
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      How to Code by Yourself (part 2)
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    • videocam
      What order should I take your courses in? (part 1)
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      What order should I take your courses in? (part 2)
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    • videocam
      Python 2 vs Python 3
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Authors

Kevin Gautama is a systems design and programming engineer with 16 years of expertise in the fields of electrical and electronics and information technology.

He teaches at the Hanoi University of Industry in the period 2003-2011 and he has a certificate of vocational training by the Ministry of Industry and Commerce and the Hanoi University of Industry.

From extensive design experience through numerous engineering projects, the author founded the Enziin Academy.

The Enziin Academy is a startup in the field of educational, it's core goal is to training design engineers in the fields technology related.

The Enziin Academy is headquartered in Stockholm-Sweden with an orientation operating multi-lingual and global.

The author's skills in IT:

  • Implementing the application infrastructure on Amazon's cloud computing platform.
  • Linux server system administration (Sysadmin).
  • Design load balancing and content distribution system.
  • MySQL database administration.
  • C/C++/C# Programming
  • Ruby and Ruby on Rails Programming
  • Python and Django Programming
  • The WPF/C# on the .NET Framework Programming
  • The PHP/JAVA Programming
  • Machine Learning and Expert System.
  • Internet of Things.

The author's skills in the fields of electric and electronic:

  • The design of popular CPU / MCU systems.
  • Design FPGA / CPLD system (Xilinx - Altera).
  • Design and programming of DSP systems (Texas Instruments).
  • Embedded ARM system design.
  • The RTOS Programming
  • Design and programming electronic power systems.
  • PLC - inverter - sensor - electric control cabinet industrial.
  • Control systems distributed connection with Server.

Read more...

Recommender Systems and Deep Learning in Python


Recommender Systems and Deep Learning in Python

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