Keras Tutorial 2021 | Creating Deep Learning Models Using Keras | Deep Learning | Simplilearn

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Learn to implement neural networks faster and easier with this tutorial on Keras! Learn how to prepare data from scratch, make, train, and compile a neural network and build your fast mask detection model! Keras tutorial for beginners, Keras for deep learning, deep learning tutorial, Keras tutorial, Keras tutorial for beginners, Keras tutorial TensorFlow, Keras tutorial python, Keras tutorial deep learning, Keras tutorial image classification, Keras neural network tutorial, Keras model tutorial, Keras layers tutorial, Keras explained, keras TensorFlow tutorial, keras python, keras example, keras install, keras tutorial CNN, CNN tutorial, convolutional neural network tutorial, deep learning tutorial, simplilearn

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What Is Keras?
Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to simplify the implementation of neural networks. It also supports multiple backend neural network computations. 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 backends for computation purposes. Keras allows you to switch between different back ends.

Why Do We Need Keras?
✅ Keras is an API that was made easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user errors clearly.
✅ Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs.
✅ The research community for Keras is vast and highly developed. The documentation and help available are far more extensive than other deep learning frameworks.
✅ Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc., which have deployed products in the public domain built using Keras.

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3 سال پیش در تاریخ 1400/08/17 منتشر شده است.
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