Keras features and reviews of 2020
Keras Deep Learning software is a high-level neural networks library created in Python that provides a scikit-learn type API and runs on either Theano or TensorFlow.
Keras Deep Learning software, as an API, is built for human beings and not just machines. The software provides developers with a scikit-learn type API used to build neural networks. Developers do not have to work about the mathematical aspects of optimization methods, numerical techniques, and tensor algebra. Also, the software is built on TensorFlow 2.0 and can scale to entire TPU pods or a large cluster of GPUs.
The idea behind the software is to facilitate experimentations by allowing developers to carry out quick prototyping. They can start from a simple idea and achieve results with little delay, which is key for research. With this software, beginner developers and scientists are at a huge advantage. They can delve right into Deep Learning and not worry about low-level computations.
The demand for Deep Learning has risen because organizations are looking for people skilled in deep learning. They are continually trying to incorporate Deep Learning into their business activities. Keras software provides an intuitive-enough-to-understand and easy-to-use API that helps these organizations to build and test Deep Learning applications with the least substantial efforts.
The demand for deep learning research has grown, and Keras offers scientists the necessary tool to try out numerous ideas without wasting time developing a neural network model.
The software also follows best practices for minimizing cognitive load. It provides simple and consistent APIs and reduces the number of user actions necessary for conventional user cases. Also, Keras software provides actionable and clear messages with extensive developer guides and documentation.
Keras Deep Learning software provides users with prelabeled datasets. Developers and scientists have access to a ton of prelabeled datasets that they can load and import directly. Some of the popular datasets available on this software include MNIST handwritten digit dataset, Reuters newswire topics classification, IMDB movie review sentiment classification, and CIFAR10 small image classification.
Keras Deep Learning software supports multiple implemented parameters and layers. The software offers numerous implemented parameters and layers like evaluations metric, optimizer, and loss functions. Also, developers use these layers for the evaluation, training, configuration, and construction of neural networks. Also, they can load the necessary layers to build their own digit classifier.
The software also supports recurrent neural nets and 1D and 2D convolutions. And, for a digital classifier, users can use Convolution neural nets.
Keras Deep Learning software offers developers numerous methods for data preprocessing. Users have to normalize and reshape datasets to meet their requirements. Once that is done, they have access to the tons of methods of data preprocessing offered by the software. For example, if they want to carryout one-hot encoding of y_test and y_train, they can use the Keras.np_utils.to_categorical() method.
Keras .add() method is used to add imported layers by specifying the parameters to develop digital classifiers. The .compile() is used in configuring learning processes, while the .ft() method is used in training Keras models on NumPy arrays.
Keras Deep Learning software allows users to perform a model evaluation. Developers can test their results on unseen data after training Keras models. They can also evaluate their models using some of the methods offered by the software. Keras software is also modular. Hence, developers can save the model they trained and use it later once it is loaded.
Keras Deep Learning software supports a vibrant community. Users have access to an active community and developer support. Also, they can get their hands on tons of tutorials on using the software.
Keras Deep Learning software can be integrated with Scikit-learn. Developers can carryout this integration easily and apply functions from Keras on scikit-learn. Some of these functions include stacking, cross-validation, and ensembles.
Keras Deep Learning software follows best practices to reduce cognitive load. This feature makes the software as simple and consistent as possible. As am API designed for developers, the software reduces the number of developer actions required for ordinary use cases. Also, developers get actionable and precise error messages when prototyping.
Keras Deep Learning software allows users to iterate at the speed of thought. Being an easy-to-use API, the software allows developers to run new experiments as they think of it. They can quickly try out more ideas without wasting time.
Keras Deep Learning software supports exascale matching learning. The software is built on TensorFlow 2.0, making it a robust framework. Users can use it to scale to an entire TPU pod or large clusters of GPUs.
Keras Deep Learning software supports a vast ecosystem. As a core part of the close-knitted TensorFlow 2.0 ecosystem, the software covers every stage of the matching learning workflow. Users can get the necessary help in deployment solutions, hyperparameter training, and data management.
Keras Deep Learning software can be used for state-of-the-art research. Many scientific organizations across the globe, like NIH, NASA, and CERN, are making use of the software. This is because it has low-level flexibility, which can be used to implement random research ideas. It also offers high-level convenience features that scientists can use in speeding up experimentation cycles.
Keras Deep Learning software can be used to teach deep learning. With a focus on user experience and being easy-to-use, the software is used as a deep learning solution for university courses. It can be used to teach university students about deep learning.
Keras Deep Learning software is an intuitive and easy-to-use neural network library written in Python. Being high-level, the software functions as a wrapper to low-level libraries like Theano or TensorFlow. With little knowledge of Deep Learning and a few lines of code, developers can easily carry out prototyping without worrying about the mathematics that comes with low-level computations.