Deep Learning - What is Keras and Tensorflow?

Keras, is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow. Microsoft Cognitive Toolkit. R. Theano. or PlaidML. Keras was designed to enable fast experimentation with deep neural networks. it focuses on being user-friendly. Modular. and extensible. It was developed as part of the research effort of project Open-ended Neuro-Electronic Intelligent Robot Operating System. and its primary author and maintainer is François Chollet, a Google engineer. Chollet also is the author of the XCeption deep neural network model.

In 2017, Google's TensorFlow team decided to support Keras in TensorFlow's core library. Chollet explained that, Keras was conceived to be an interface rather than a standalone machine learning framework. It offers a higher-level. more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. Microsoft added a CNTK backend to Keras as well, available as of CNTK version 2.0.

Keras contains numerous implementations of commonly used neural-network building blocks. such as layers, objectives, activation functions, optimizers, and a host of tools to make working with image and text data easier to simplify the coding necessary for writing deep neural network code. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel.

In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling.
Keras allows users to productize deep models on smartphones, (IOS and Android), on the web, or on the Java Virtual Machine. It also allows use of distributed training of deep-learning models on clusters of Graphics processing units (GPU) and tensor processing units (TPU) principally in conjunction with CUDA.
Keras vs Tensorflow – Whats the difference
Keras is a neural network library while TensorFlow is the open source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. ... Keras is built in Python which makes it way more user-friendly than TensorFlow.
Tensorflow is the most famous library used in production for deep learning models. However TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow.
Keras is an API designed for human beings, not machines. ... This makes Keras easy to learn and easy to use. As a Keras user, you are more productive, allowing you to try more ideas than your competition, faster -- which in turn helps you win machine learning competitions.
you can use TensorFlow without Keras, essentially building the model “by hand” and taking care of all the gory details yourself; but you cannot use Keras without an underlying engine.