tensorflow
- Neural translation model
- Tracking metrics in custom training loops
- The build method, allowing flexible inputs for custom layers
- Residual network
- Model subclassing and custom training loops
- Tokenizing text Data
- Stateful RNNs
- Sequence Modelling
- Language model for the Shakespeare dataset
- Data generators for time series
- Tensorflow Datasets
- Data pipeline with Keras and tf.data
- Data Pipeline
- Creating Datasets from different sources
- Transfer learning
- The Keras functional API
- Layer nodes
- Device placement
- Image classifier for the SVHN dataset
- Saving model architecture only
- Saving and loading models
- Saving and loading model weights
- Explanation of saved files
- Validation, regularization, callbacks
- The logs dictionary
- Model validation on the Iris dataset
- Batch normalization
- Additional callbacks
- Introduction to TensorFlow
- The Sequential model API
- Optimizer, loss functions, metrics
- CNN classifier for the MNIST dataset
- Adding weight initialisers
Coursera
- Neural translation model
- Tracking metrics in custom training loops
- The build method, allowing flexible inputs for custom layers
- Residual network
- Model subclassing and custom training loops
- Tokenizing text Data
- Stateful RNNs
- Sequence Modelling
- Language model for the Shakespeare dataset
- Data generators for time series
- Tensorflow Datasets
- Data pipeline with Keras and tf.data
- Data Pipeline
- Creating Datasets from different sources
- Transfer learning
- The Keras functional API
- Layer nodes
- Device placement
- Image classifier for the SVHN dataset
- Saving model architecture only
- Saving and loading models
- Saving and loading model weights
- Explanation of saved files
- Validation, regularization, callbacks
- The logs dictionary
- Model validation on the Iris dataset
- Batch normalization
- Additional callbacks
- Introduction to TensorFlow
- The Sequential model API
- Optimizer, loss functions, metrics
- CNN classifier for the MNIST dataset
- Adding weight initialisers