Introduction
Machine learning is a set of computer algorithms that learns the pattern from the data and evolves on its own as time passes. Deep learning is popular due to its reliability in terms of accuracy. Whether we want to start applying it to the business based on the next side project or gain marketable skills, picking the proper deep learning framework to learn is an essential step towards reaching the goal. The three most popular frameworks for deep learning are Keras, TensorFlow, and PyTorch.
Also Read, Resnet 50 Architecture
Keras
source:Link
- Keras is an open-source neural network library written in Python. Keras can run on top of the TensorFlow Microsoft cognitive toolkit or Theano. It enables fast experimentation with deep neural networks. It also focuses on being user-friendly, modular, and extensible.
- Keras has a high-level API capable of running on top of TensorFlow and Theano. It has gained favor for its syntactic simplicity facilitating fast development.
- The Speed of Keras is slower as compared to TensorFlow and PyTorch.
- Kears has simple architecture. It is more readable and concise; It is simple and easy to use, so most beginners prefer to use Keras compared to the other two.
- There is a single line of code for implementing it, which makes it a preferable framework for programmers.
- In Keras, there is significantly less frequent need to debug simple networks, and it offers a more direct unconvoluted debugging experience regardless of model complexity.