7 popular tools and frameworks for developing AI applications
Artificial Intelligence (AI) is a rapidly growing field with numerous applications, including computer vision, natural language processing (NLP) and speech recognition. To develop these AI applications, developers use various tools and frameworks that provide a comprehensive platform for building and deploying machine learning models.
This article will discuss the seven popular tools and frameworks used for developing AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet. These tools have become the go-to choice for developers thanks to their ease of use, scalability and efficient execution of complex mathematical operations.
Fabric for Deep Learning (#FfDL), an #opensource project that provides developers the flexibility to use popular deep learning libraries such as #TensorFlow, #Caffe, and #Torch https://t.co/DpTv5uRcDE pic.twitter.com/VozsIXPk8n
TensorFlow is an open-source platform developed by Google, which provides a comprehensive framework for building and deploying machine learning models across multiple platforms. It is widely used for various applications, including computer vision, natural language processing and speech recognition. For example, it can be used to build a chatbot that can understand and respond to natural language queries.
With @TensorFlow, this expert is building groundbreaking machine learning models in image and speech recognition → https://t.co/o2GMG9yYeu Learn how ML #GDE and #WTMAmbassador Ruqiya Bin Safi provides a reminder that with focus and time, you can thrive on your #DevJourney! pic.twitter.com/NXAhyZyx69
PyTorch is another popular open-source machine learning framework, widely used for developing AI applications such as image
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