Deep Learning with Keras

A warm welcome to the Deep Learning with Keras course by Uplatz.

Keras is an open–source library of neural network components written in Python. Keras is capable of running atop TensorFlow, Theano, PlaidML and others. The library was developed to be modular and user–friendly. Keras enables fast experimentation through a high level, user–friendly, modular and extensible API. Keras can also be run on both CPU and GPU. Keras was developed and is maintained by Francois Chollet and is part of the TensorFlow core, which makes it TensorFlow preferred high–level API.

Comprised of a library of commonly used machine learning components including objectives, activation functions, and optimizers, Keras’ open–source platform also offers support for recurrent and convolutional neural networks. Additionally, Keras offers mobile platform development for users intending to implement deep learning models on smartphones, both iOS and Android.

Keras is essentially an API designed for machine learning and deep learning engineers and follows best practices for reducing cognitive load. Keras offers consistent & simple APIs, minimizes the number of user actions required for common use cases, and provides clear & actionable error messages. It also supports extensive documentation and developer guides.

It is made user–friendly, extensible, and modular for facilitating faster experimentation with deep neural networks. It not only supports Convolutional Networks and Recurrent Networks individually but also their combination