Introduction To Deep Learning With NVIDIA GPUs

Overview

Organizations are using deep learning and AI at every stage of growth, from start-ups to Fortune 500s. Deep learning, the fastest growing field in AI, is empowering immense progress in all kinds of emerging markets and will be instrumental in ways we haven’t even imagined. Today’s advanced deep neural networks use algorithms, big data, and the computational power of the GPU to change this dynamic.

Machines are now able to learn at the speed, accuracy and scale that are driving true artificial intelligence and AI Computing.

Learn the latest techniques on how to design, train, and deploy neural network-powered machine learning in your applications. You’ll explore widely used open- source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.

Who Should Attend

Anyone interested in to learn more about Deep Learning, or kickstart a career as a Data Scientist. This includes Students, Data Analysts, Business Owners, Entrepreneurs or any individual who wishes to leverage on powerful Deep Learning tools to add value wherever they are.

Prerequisites

Basic high school mathematics knowledge, no Prior Deep Learning knowledge. Basic Python understanding can be used for some exercise.

Course Outline

• What is Deep Learning and what are Neural Networks?
• Artificial Neural Networks (ANN) Intuition
• Building an ANN
• Evaluating Performance of an ANN
• Building a CNN
• Convolutional Neural Networks (CNN)
• Evaluating Performance of a CNN
• Recurrent Neural Networks (RNN) Intuition
• Building a RNN
• Evaluating Performance of a RNN
• Getting Started with Deep Learning
• The biological inspiration for Deep Neural
• Networks (DNNs)
• Training DNNs with Big Data
• Deploying DNN Models
• Optimizing DNN Performance
• Incorporating Object Detection