How to become a Full-Stack Deep Learning Engineer by Forrest Iandola

Описание: Historically, AI has been an algorithm-centric field. However, with the rise of Deep Neural Networks (DNNs), it is now the case that (1) large-scale data, (2) novel DNN models, and (3) efficient software and hardware infrastructure, are all key to success. The best outcomes often come from teams who understand the "full stack" from low-level hardware for DNNs to high-level applications of DNNs. Full-stack DNN teams are able to make big-picture tradeoffs in the development of data, models, and infrastructure, leading to practical solutions that exhibit unprecedented levels of accuracy, speed, energy-efficiency.

In this talk, Forrest Iandola focuses on three main topics. First, he inclusively defines the "full stack" of skills and technologies that go into DNN engineering. Second, he describes a playbook for managers who want to build, coach, and grow a full-stack DNN engineering team. This playbook draws on lessons that Forrest have learned first-hand at UC Berkeley, Microsoft Research, and DeepScale. Finally, he provides advice on how a generalist or specialist engineer can engage with a full-stack DNN engineering team, and describes a path for how to ultimately become a full-stack DNN engineer.

Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on deep neural networks. His advances in scalable training and efficient implementation of deep neural networks led to the founding of DeepScale, where he is CEO. DeepScale is focused entirely on building perception systems for automated vehicles, and DeepScale has a number of engagements with automakers and automotive suppliers.