Applying Convolutional Neural Networks with TensorFlow

You need greater efficiency, agility, scalability, and cost-effectiveness from your IT infrastructure. Legacy storage solutions just aren’t cutting it anymore! What many IT Organizations don’t know is that there are many different solutions available to solve these IT challenges! The one thing that all businesses have in common is that they want and need IT solutions to make their lives easier, more efficient, and more affordable, particularly as digital transformation efforts take center stage. Converged, hyperconverged, composable, and other integrated platforms (collectively, integrated systems) all have the potential to accomplish these goals which can allow the IT organization to focus their efforts more on business outcomes rather than hardware.

Decisions made at the national and state level are affecting local communities all around the country. But these communities vary greatly. How do local leaders take control to ensure the right decisions are made for their communities?

What if you could directly ask questions of your data? Ask Data, Tableau’s new natural language capability, allows people to get insights by simply conversing with their data. In this latest Data Science Central webinar, members of Tableau’s Ask Data team will demonstrate how they are lowering the barrier to analytics and leveraging Natural Language Processing (NLP) as a tool for visual analysis. Looking to quickly make smarter, data-informed decisions and empower others to do the same? Watch this detailed overview of Ask Data’s capabilities to learn more.

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.