A typical CNN architecture consists of multiple layers that do different tasks, as shown in the preceding diagram. In this section, we are going to go through them in detail and will see the benefits of having all of them connected in a special way to make such a breakthrough in computer vision.
- 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
Different layers of CNNs
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