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Deep Learning 101 – The rise of the GPU
This series gives you a quick overview of several deep learning frameworks. With each framework, you’ll learn about the framework’s benefits, supported platforms, installation considerations, and supported back ends. Deep learning isn’t a single approach but rather a class of algorithms and topologies that you can apply to a broad spectrum of problems. While deep learning is certainly not new, it is experiencing explosive growth because of the intersection of deeply layered neural networks and the use of GPUs to accelerate their execution. Big data has also fed this growth. Because deep learning relies on supervised learning algorithms, the more data, the better to build these deep learning structures. This workshop walks you through the fundamentals of Deep Learning and Artificial Neural Networks and explains how to train a deep learning language model in a notebook using Keras and TensorFlow. Use case: Using downloaded data from Yelp, you’ll learn how to install TensorFlow and Keras, train a deep learning language model, and generate new restaurant reviews. While the scope of this is limited to an introduction to text generation, it provides a strong foundation for learning how to build a language model. We’ll demonstrate how choosing the right hardware platform (based on the given use-cases) will expedite mining data, training your model and increasing accuracy. We’ll also compare CPU vs. GPU performance for deep learning applications. In particular we’ll focus on credit card-sized Nvidia Jetson TX2 with 256 CUDA Cores to Nvidia Xaviar containing 512 Volta Cores and finally full-blown Geforce RTX with nearly 3000 CUDA cores. Speaker : Mo Haghighi : Head of Developer Ecosystems Group,IBM Agenda: 18.30 - 19.00 : Registration, Food, Drinks and Networking 19.00 - 21.00 : Introduction, followed by a walkthrough of the Use Case

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