Top 5 Deep Learning Frameworks

Deep Learning is a subfield of Machine Learning based on Artificial Neural Networks. Unsupervised Pretrained Networks (UPNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks, and Recursive Neural Networks are the four major network architectures in Deep Learning. One of the main differences between Deep Learning vs Machine Learning is the way data is delivered to the system. It has more computational power making it one of the powerful and important Machine Learning subsets.

There are many Deep Learning Frameworks; however, we are going to review and list the most preferred frameworks.

TensorFlow
Developed and maintained by Google, Tensorflow is the best and most popular Deep Learning framework. It can be used for building and deploying Deep Learning models. It can be used with Keras to build Neural Networks line-by-line.

PyTorch
Primarily developed by Facebook’s AI Research lab, PyTorch is another library used to develop and train Neural Networks. It is a Python package and can be used with Python and C++. It is one of the preferred Deep Learning libraries for researchers.

Keras
Keras is a Python-based open-source neural-network library. It is a high-level API built on Theano and Tensorflow and is considered to be more user-friendly.

MXNet
Apache MXNet is an open-source deep learning software framework developed by the Apache Software Foundation. MXNet stands for Mix and Maximize. Its fast problem-solving ability makes it an efficient Deep Learning library to train and deploy Neural Networks.

Caffe
Written in C++ and Python, Caffe is a CAFFE is a Deep Learning framework developed by Berkeley AI Research (BAIR). Caffe is primarily designed for Speed, making it a perfect choice for research experiments and industry deployments.

What’s next?
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