Deep learning is hard. Between organizing, cleaning and labeling data, selecting the right neural network topology, picking the right hyperparameters, and then waiting – hoping – that the model produced is accurate enough to put into production. It can seem like an impossible puzzle for your data science team to solve. But the IT aspect of the puzzle is no less complicated, especially when the environment needs to be multi-user and support distributed model training. From choosing an operating system, to installing libraries, frameworks, dependencies, and development platforms, building the infrastructure to support your company’s deep … READ MORE
Source: Dell Blog
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