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Top AI/ML frameworks and libraries

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Artificial Intelligence (AI) is the top technology trend that everyone is talking about. More and more developers are trying to get their hands dirty with AI frameworks as they see the growing demand in this field. Machine Learning (ML) and Deep Learning (DL) are part of a broader AI umbrella that is witnessing tremendous growth. When you start exploring this field, you must be asking yourself, "what AI/ML/DL frameworks and libraries that I should be familiar with?". Well, here is the list of frameworks and libraries. It is Google's open-source end-to-end machine learning platform to help you develop, train, validate, and deploy models in large production environments.


Top 10 Deep Learning Frameworks for Every Data Scientist

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Deep learning framework with an interface or a library/tool helps Data Scientists and ML Developers to bring the deep learning models into life. Deep Learning a sub-branch of machine learning, that puts efficiency and accuracy on the table, when it is trained with a vast amounts of bigdata. TensorFlow developed by the Google Brain team, is inarguably one of the most popular deep learning frameworks. It supports Python, C, and R to create deep learning models along with wrapper libraries. It is available on both desktop and mobile. The most popular use case of TensorFlow is the Google Translate integrated with capabilities like NLP, text classification, summarization, speech/image/handwriting recognition and forecasting.


The top 10 deep learning frameworks PACKT Books

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This is the age of artificial intelligence. Machine Learning and predictive analytics are now established and integral to just about every modern businesses, but artificial intelligence expands the scale of what's possible within those fields. It's what makes deep learning possible. Systems with greater ostensible autonomy and complexity can solve similarly complex problems. If Deep Learning is able to solve more complex problems and perform tasks of greater sophistication, building them is naturally a bigger challenge for data scientists and engineers.


8 Deep Learning Frameworks for Data Science Enthusiasts

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AI coupled with the right deep learning framework has truly amplified the overall scale of what businesses can achieve and obtain within their domains. The machine learning paradigm is continuously evolving. The key is to shift towards developing machine learning models that run on mobile in order to make applications smarter and far more intelligent. Deep learning is what makes solving complex problems possible. As put in this article, Deep Learning is basically Machine Learning on steroids.


Top 10 deep learning Framesworks everyone should know

#artificialintelligence

This is the age of artificial intelligence. Machine Learning and predictive analytics are now established and integral to just about every modern businesses, but artificial intelligence expands the scale of what's possible within those fields. It's what makes deep learning possible. Systems with greater ostensible autonomy and complexity can solve similarly complex problems.