Fundamentals of Deep Learning for Multi-GPUs

By NVIDIA Deep Learning Institute

Learning Objectives

At the conclusion of the workshop, you’ll have an understanding of:

• Stochastic gradient descent (SGD), a crucial tool in parallelized training
• Batch size and its effect on training time and accuracy
• Transforming a single-GPU implementation to a Horovod multi-GPU implementation
• Techniques for maintaining high accuracy when training across multiple GPUs

Technologies: TensorFlow, Keras, Horovod

Contact

Anurag Dogra - anuragdogra.2192@gmail.com