We're going to format the API just a little differently here than we did in the previous example. This slightly different structure will make the using of pretrained vectors in the embedding layer a little bit easier. We will discuss these structural changes in the following sections.
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- The Building Blocks of Deep Learning
- Using Deep Learning to Solve Regression Problems
- Monitoring Network Training Using TensorBoard
- Using Deep Learning to Solve Binary Classification Problems
- Using Keras to Solve Multiclass Classification Problems
- Hyperparameter Optimization
- Training a CNN from Scratch
- Transfer Learning with Pretrained CNNs
- Training an RNN from scratch
- Training LSTMs with Word Embeddings from Scratch
- Training Seq2Seq Models
- Using Deep Reinforcement Learning
- Generative Adversarial Networks
- Other Books You May Enjoy
Input and embedding layer architecture
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