Nvidia

Fundamentals of Deep Learning for Computer Vision Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities. In this course, you’ll learn the basics of deep learning by training and deploying neural …

Image Classification with DIGITS Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognise handwritten digits by: Loading image data to a training environment Choosing and training a …

Neural Network Deployment with DIGITS and TensorRT Deep learning lets us map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognise images and detect pedestrians in real-time by: Accessing and understanding the …

Deep Learning Workflows with TensorFlow, MXNet and NVIDIA-Docker The NVIDIA Docker plugin makes it possible to containerize production-grade deep learning workflows using GPUs. Learn to reduce host configuration and administration by: Learning to work with Docker images and manage the …

Image Segmentation with TensorFlow Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. Learn how to segment MRI images to measure parts of the heart by: Comparing image segmentation with other …

Image Classification with Microsoft Cognitive Toolkit Learn to train a neural network using the Microsoft Cognitive Toolkit framework. You’ll build and train increasingly complex networks to: Compare the expression of a neural network using BrainScript’s “Simple Network Builder” vs. the …

Linear Classification with TensorFlow In this tutorial, we will use the TF.Learn API in TensorFlow to solve a binary classification problem: Given census data about a person such as age, gender, education and occupation (the features), we will try to …

Signal Processing with DIGITS Deep neural networks are better at classifying images than humans, which has implications beyond what we expect of computer vision. Learn how to convert radio frequency (RF) signals into images to detect a weak signal corrupted …

Fundamentals of Accelerated Computing with CUDA C/C++ The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by: Accelerating CPU-only applications to run their latent parallelism on …