In this chapter, we introduced a totally new architecture that can be used for many interesting applications. Autoencoders are very flexible, so feel free to come up with your own problem in the area of image enhancement, colorization, or construction. Also, there are more variations of autoencoders, called variational autoencoders. They are also used for very interesting applications, such as image generation.
- Title Page
- Copyright and Credits
- Packt Upsell
- Contributors
- Preface
- Data Science - A Birds' Eye View
- Data Modeling in Action - The Titanic Example
- Feature Engineering and Model Complexity – The Titanic Example Revisited
- Get Up and Running with TensorFlow
- TensorFlow in Action - Some Basic Examples
- Deep Feed-forward Neural Networks - Implementing Digit Classification
- Introduction to Convolutional Neural Networks
- Object Detection – CIFAR-10 Example
- Object Detection – Transfer Learning with CNNs
- Recurrent-Type Neural Networks - Language Modeling
- Representation Learning - Implementing Word Embeddings
- Neural Sentiment Analysis
- Autoencoders – Feature Extraction and Denoising
- Generative Adversarial Networks
- Face Generation and Handling Missing Labels
- Implementing Fish Recognition
- Other Books You May Enjoy
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