12 data science mistakes to avoid

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IDG AI/Machine Learning Report Our top picks from IDG's editors May 10, 2018 12 data science mistakes to avoid Well-managed analytics initiatives can reap organizational gold. But succumb to one of these common mistakes, and your data science operations can quickly go wrong. Read More ▶ Your Must-Read Stories • Why you should use Python for machine learning • Real-time AI gets closer with Project Brainwave • What's new in TensorFlow machine learning • ML Kit: Google brings machine learning APIs to mobile developers • The new data roles, brought to you by AI Why you should use Python for machine learning Learn why Python has become the go-to programming language for machine learning and deep learning applications Read More ▶ Real-time AI gets closer with Project Brainwave Microsoft's Project Brainwave extends Azure ML to datacenter and network hardware, to bring the machine learning processing where the data is Read More ▶ What's new in TensorFlow machine learning Google's TensorFlow 1.8 machine learning library adds improved support for Google Cloud TPUs and prefetching to GPU memory Read More ▶ ML Kit: Google brings machine learning APIs to mobile developers The beta machine learning kit supports iOS and Android apps via the Firebase IDE and with TensorFlow Lite support Read More ▶ The new data roles, brought to you by AI Artificial intelligence is providing market competitiveness through higher productivity and lower costs across many industries. AI is also creating new data roles companies are desperate to fill; read more about these new roles. Read More ▶

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