TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. If you’re starting a new machine learning or deep learning ...
The power of machine learning comes at a price. Once you have the skills, the toolkit, the hardware, and the data, there is still the complexity involved in creating and fine-tuning a machine learning ...
Over the past year I’ve reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and ...
Development tools like the DeepView ML tool suite, Glow ML compiler, and PyTorch framework simplify the process of creating ML/deep-learning projects on embedded platforms and help developers bring ...
A behind-the-scenes blog about research methods at Pew Research Center. For our latest findings, visit pewresearch.org. Social scientists are increasingly adopting machine learning methods to analyze ...
Seven-month LIVE online programme, delivered with TimesPro, builds hands-on capability in Python, TensorFlow, PyTorch, and ...
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