With more and more businesses looking to scale up their operations, it has become integral for them to imbibe both machine learning as well as predictive analytics. 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.
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.
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.
Caffe 2 continues the strong support for vision type problems but adds in recurrent neural networks (RNN) and long short term memory (LSTM) networks for natural language processing, handwriting recognition, and time series forecasting. MXNet supports deep learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) including Long Short-Term Memory (LTSM) networks. This framework provides excellent capabilities for imaging, handwriting and speech recognition, forecasting and natural language processing. DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.
In "Big data – a road map for smarter data," I describe a set of machine learning architectures that will provide advanced capabilities to include image, handwriting, video, and speech recognition, natural language processing and object recognition. There is no perfect deep learning network that will solve all your business problems. Hopefully, the below table with the accommodating descriptive outline will provide you insights towards the best fit for purpose framework for your business problem. The ranking is based on the number of stars awarded by developers in GitHub. The numbers were compiled at the beginning of May of 2017.