Part 6 - AutoEncoders
Welcome to Part 6 - AutoEncoders
- In this part you will learn:
- The Intuition of AutoEncoders
- How to build an AutoEncoder from scratch with PyTorch
- How to manipulate classes and objects to improve and tune your AutoEncoder
- Boltzmann Machines can be seen from two different points of view:
- An Energy-Based Model
- A Probabilistic Graphical Model
In the previous part we created a Recommender System that predicted binary ratings “Like” or “Not Like”. In this part we will take it at the next level and create a Recommender System that predicts ratings from 1 to 5.
- We will implement a Stacked AutoEncoders model with PyTorch, a highly advanced Deep Learning platform more powerful than Keras. Every single line of code will be explained in details but I would recommend to have a first look at the PyTorch documentation to start getting familiar with PyTorch:
Enjoy Deep Learning!
Plan of Attack
- What we will learn in this section:
- Auto Encoders
- Training of an Auto Encoder
- Overcomplete Hidden Layers
- Sparse AutoEncoders
- Denoising AutoEncoders
- Contractive AutoEncoders
- Stacked AutoEncoders
- Deep AutoEncoders
Read More about AutoEncoders»
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