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Keras tends to be highly significant for beginners beginning with neural networks. This is a higher-level framework that was formed with an emphasis on quick experimentation. With it, you can develop a neural network model easily.

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What is Keras?

Keras is an open-source neural network library that is written in Python. It runs on top of Tensorflow or Teheano. This is designed for being easy, fast, and modular for use. François Chollet, an engineer from Google developed Keras.


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The remarkable thing is Keras does not handle the computation of lower level and in place of that, it utilizes another library for doing it known as “Backend”. Hence, Keras is a high-level API wrapper that serves the low-level API.

Keras does compile a model with loss as well as optimizer functions, fit function, and training process. Keras does not even deal with low-level API, like forming the computational graph, create tensors besides other variables as it gets handled by a backend engine.

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Features of Keras

Keras comprises countless implementations of usually used neural-network forming blocks, like objectives, layers, optimizers, activation functions, and several tools for turning the job with text and image data easier. This simplifies the coding important for writing deep neural network code. GitHub hosts this code and community support forums comprise a Slack channel and GitHub issues page.

Besides normal neural networks, Keras does support recurrent and convolutional neural networks and it does support other common utility layers, such as batch normalization, pooling, and dropout. With the help of Keras, users can productize deep models on Android and iOS devices, on the Java Virtual Machine, and on the web. This also allows the utilization of distributed training of some deep-learning models on the clusters of TPU (tensor processing units) and GPU (Graphics processing units).

Benefits of Keras

  • Deep learning for humans – Keras is recognized as an API intended for humans and not machines. It follows the finest practices for lessening the cognitive load. Additionally, it proposes simple and consistent APIs. It lessens the user actions needed for common utilization cases. Additionally, it proposes actionable and clear error messages.
  • Repeat at the thought’s speed – Keras is the highly utilized deep-learning framework amongst top- winning teams. As Keras turns it easier for running novice experiments, it does empower you to attempt more and more ideas in comparison to your competition, and that too pretty faster.
  • Keras develops novice training processes – People can use Keras to form novice training processes or striking model architectures quickly.

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