Explore generative models in AI with Keras

Explore Keras, a high-level API for building and training deep learning models, with recurrent, convolutional, and fully connected layers.

Key takeaways

Keras is a high-level API for building and training deep learning models, and its mission is to make practical AI accessible to everyone.
Keras provides three ways to instantiate a model: sequential API, functional API, and subclassing a model.
The sequential API is the simplest way to build a model, but it can be inflexible.
The functional API provides more flexibility, but requires more code.
Subclassing a model is the most flexible way to build a model, but requires a good understanding of the underlying mathematics.
Keras provides several layers, including convolutional layers, recurrent layers, and fully connected layers.
The functional API can be used to build complex models, such as those that involve multiple inputs and outputs.
Keras also provides several optimizers, including stochastic gradient descent and Adam.
The model builder API can be used to build and train models in a flexible and efficient way.
Keras supports several types of models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
Keras also supports several types of data, including images, text, and audio.
The model object in Keras is used to represent a deep learning model, and it provides several methods for building and training the model.
The fit method is used to train a model, and it takes several arguments, including the training data, the number of epochs to train for, and the batch size.
The predict method is used to make predictions with a trained model, and it takes several arguments, including the input data and the number of predictions to make.
Keras also provides several callbacks, including the EarlyStopping callback, which is used to stop training early if the model’s performance on the validation set stops improving.
The ModelCheckpoint callback is used to save the model at regular intervals during training.
The TensorBoard callback is used to visualize the model’s performance and make adjustments to the training process.
Keras also provides several tools for building and training models, including a built-in optimizer and several layers.

Explore generative models in AI with Keras

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