Presented By
GTCx LogoMenu
Presented by

Deep Learning Training

Presented by

Train directly with NVIDIA Deep Learning Institute trained instructor-led labs at GTCx Australia. Learn how advanced deep learning techniques are being applied to rich data sets in order to help solve big problems. Upon completion of an NVIDIA Deep Learning Institute training lab, you will receive a certificate of attendance and free online training credits.

  • Getting Started with Deep Learning

    Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data. This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a challenging real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You’ll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset.

  • Deep Learning for Object Detection

    Building upon the foundational understanding of how deep learning is applied to image classification, this lab explores different approaches to the more challenging problem of detecting if an object of interest is present within an image and recognizing its precise location within the image. Numerous approaches have been proposed for training deep neural networks for this task, each having pros and cons in relation to model training time, model accuracy and speed of detection during deployment. On completion of this lab you will understand each approach and their relative merits. You’ll receive hands-on training applying cutting edge object detection networks using NVIDIA DIGITS on a challenging real-world dataset.

  • Deep learning Network Deployment

    There are a variety of important applications that need to go beyond detecting individual objects within an image and instead segment the image into spatial regions of interest. For example,in medical imagery analysis it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells so that we can isolate a particular organ. In this lab we will use the Tensorflow deep learning framework to train and evaluate an image segmentation network using a medical imagery dataset.

  • Deep learning Image Segmentation

    Here are a variety of important applications that need to go beyond detecting individual objects within an image and instead segment the image into spatial regions of interest. For example, in medical imagery analysis it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells so that we can isolate a particular organ. In this lab we will use the TensorFlow deep learning framework to train and evaluate an image segmentation network using a medical imagery dataset.

  • Deep Learning for medical image analysis (MXNet)

    Convolutional neural networks (CNNs) have proven to be just as effective in visual recognition tasks involving non-visible image types as regular RGB camera imagery. One important application of these capabilities is medical image analysis where we wish to detect features to provide decision support. In addition to processing ionizing and non-ionizing imagery such as CT scans and MRI, these applications also often require processing higher dimensionality imagery that may be volumetric and have a temporal component. In this lab you will use the deep learning framework MXNet to train a CNN to infer the volume of the left ventricle of the human heart from a time-series of volumetric MRI data. You will learn how to extend the canonical 2D CNN to be applied to this more complex data and how to directly predict the ventricle volume rather than generating an image classification. In addition to the standard Python API you will also see how to use MXNet through R which is an important data science platform in the medical research community.

Deep Learning Institute

See what the Deep Learning Institute program is all about. Learn how advanced deep learning techniques are being applied to these rich datasets to help solve problems.

Deep Learning Self-Paced Course

Learn everything you need to design, train, and integrate neural network powered machine learning into our applications with widely used open-source frameworks and the NVIDIA deep learning platform.