Popular Deep Learning Frameworks: An Overview

Popular Deep Learning Frameworks: An Overview

Deep learning is a broader field of machine learning, which uses artificial neural networks(ANN) to derive high-level features from the inputs. Deep learning or deep neural networks(DNN) architecture consists of multiple layers, specifically the hidden layers between the input and output layers.

Deep learning is a broader field of machine learning, which uses artificial neural networks(ANN) to derive high-level features from the inputs. Deep learning or deep neural networks(DNN) architecture consists of multiple layers, specifically the hidden layers between the input and output layers.

Some common deep learning architectures are Convolutional neural networks(CNNs), Recurrent neural networks(RNNs), Long Short Term Memory(LSTM), deep belief networks(DBN), which have been applied to computer vision, audio and speech recognition and natural language processing(NLP) use cases. Even structured and unstructured tabular data have shown good performance using deep learning models.

In this article, I’ll discuss the deep learning frameworks available for different programming language interfaces.

A deep learning framework allows researchers and developers to achieve the state-of-art compactly and robustly. It helps in training and testing the model using APIs. These provide high-level performance and better management of dependencies.

deep learning deep learning frameworks keras tensorflow

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