Learn Keras Programming Guide ١٧+

Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly. Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides.

Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser It's also easy to serve Keras models as via a web API.

Keras is a central part of the tightly-connected TensorFlow 2 ecosystem, covering every step of the machine learning workflow, from data management to hyperparameter training to deployment solutions. Keras is used by CERN, NASA, NIH, and many more scientific organizations around the world (and yes, Keras is used at the LHC). Keras has the low-level flexibility to implement arbitrary research ideas while offering optional high-level convenience features to speed up experimentation cycles.

Because of its ease-of-use and focus on user experience, Keras is the deep learning solution of choice for many university courses. It is widely recommended as one of the best ways to learn deep learning.

Keras API reference
Models API
The Model class
The Sequential class
Model training APIs
Model saving & serialization APIs
Layers API
The base Layer class
Layer activations
Layer weight initializers
Layer weight regularizers
Layer weight constraints
Core layers
Convolution layers
Pooling layers
Recurrent layers
Preprocessing layers
Normalization layers
Regularization layers
Attention layers
Reshaping layers
Merging layers
Locally-connected layers
Activation layers
Callbacks API
Base Callback class
ModelCheckpoint
BackupAndRestore
TensorBoard
EarlyStopping
LearningRateScheduler
ReduceLROnPlateau
RemoteMonitor
LambdaCallback
TerminateOnNaN
CSVLogger
ProgbarLogger