Now, let's start off by building the core of our implementation, which is the computational graph; it will mainly include the following components:
- Model inputs
- Discriminator
- Generator
- Model losses
- Model optimizer
- Training the model
- 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
Building the model
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