Deep learning frameworks are trending among machine learning developers

Deep learning frameworks help data scientists and ML developers in various critical tasks. As of today, both predictive analytics and machine learning are deeply integrated into business operations and have proven to be quite crucial. Integrating this advanced branch of ML can enhance efficiency and accuracy for the task-at-hand when it is trained with vast amounts of big data. In this video, we will explore the top deep learning frameworks that techies should learn this year.

Tensor Flow: The Javascript-based open-source learning platform has a wide range of tools to enable model deployment on different types of devices. While the core tools facilitate model deployment on browsers, the lite version is well-suited for mobiles and embedded devices.

PyTorch: Developed by Facebook, it is a versatile framework, originally designed to explore the entire process, from research prototyping to production deployment. It carries a C++ frontend over a Python interface.

Keras: It is an open-source framework that can run on top of Tensorflow, Theano, Microsoft Cognitive Toolkit, and Plaid ML. Keras framework is known for its speed because of built-in support for parallel processing of data processing and ML training.

Sonnet: A high-level library that is used in building complex neural network structures in Tensorflow. It simplifies the high-level architectural designs by independently creating Python objects to a graph.

MXNet: It is a highly scalable open-source Deep learning framework designed to train and deploy deep neural networks. It is capable of fast model training and supports multiple programming languages such as C, C++, Python, Julia, Matlab, etc.