NVIDIA Deep Learning Institute Online Labs

Online Self-Paced Labs

The NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning.

Through self-paced online labs and instructor-led workshops, DLI provides training on the latest techniques for designing, training, and deploying neural networks across a variety of application domains. Explore widely used open-source frameworks as well as NVIDIA’s latest GPU-accelerated deep learning platforms.

Create an account to take hands-on deep learning labs online.


Applications of Deep Learning with Caffe, Theano, and Torch

Level: Beginner | Prerequisites: None
Industry: All | Frameworks: Caffe, Theano, Torch

Learn how deep learning will change the future of computing. In this hands-on session (no technical background required), you will:

  • Compare deep learning to traditional methods
  • Run training and inference with three different deep learning frameworks
  • Learn how deep learning works and why the GPU is integral

Upon completion, you will be better equipped to decide how you or your organization can get started with deep learning.

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Image Classification with DIGITS

Level: Beginner | Prerequisites: None
Industry: All | Frameworks: Caffe

Deep learning enables entirely new solutions by replacing hand-coded instructions with models learned from examples. Train a deep neural network to recognize handwritten digits by:

  • Loading image data to a training environment
  • Choosing and training a network
  • Testing with new data and iterating to improve performance

Upon completion, you will be able to assess what data you should be training from.

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Object Detection with DIGITS

Level: Beginner | Prerequisites: Image Classification with DIGITS
Industry: All | Frameworks: Caffe

Deep learning has established solutions to many problems, but sometimes a problem is unique. Create your own solution to detect whale faces from aerial images by:

  • Combining traditional computer vision with deep learning
  • Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe
  • Hiring an army of experts to build your dream network

Upon completion, you will be able to solve unique problems with deep learning.

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Neural Network Deployment with DIGITS and TensorRT

Level: Intermediate | Prerequisites: Image Classification Using DIGITS
Industry: All | Frameworks: Caffe

Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by:

  • Accessing and understanding the files that make up a trained model
  • Building from each function’s unique input and output
  • Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency

Upon completion, you will be able to implement deep learning to solve problems in the real world.

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Image Segmentation with TensorFlow

Level: Beginner | Prerequisites: Image Classification with DIGITS
Industry: All | Frameworks: TensorFlow

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. In this lab, you will segment MRI images to measure parts of the heart by:

  • Comparing image segmentation with other computer vision problems
  • Experimenting with TensorFlow tools such as TensorBoard and the TensorFlow Python API
  • Learning to implement effective metrics for assessing model performance

Upon completion, you will be able to set up most computer vision workflows using deep learning.w.

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Linear Classification with TensorFlow

Level: Beginner | Prerequisites: None
Industry: All | Frameworks: TensorFlow

Learn to make predictions from structured data using TensorFlow’s TFLearn API. Through the challenge of predicting a person’s income when given the rest of their census data, you will learn to:

  • Load, view, and organize data from a CSV for machine learning
  • Split an existing dataset into features and labels (input and output) of a neural network
  • Train and evaluate a linear model.
  • Build from linear to deep models and assess the difference in performance.

Upon completion, you will be able to make predictions from your own structured data.

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Signal Processing Using DIGITS

Level: Beginner | Prerequisites: None
Industry: All | Frameworks: Caffe

The fact that deep neural networks are better at classifying images than humans has implications beyond what we typically think of computer vision. In this lab, you will convert Radio Frequency (RF) signals into images to detect a weak signal corrupted by noise and learn:

  • How non-image data can be treated like image data
  • How to implement a deep learning workflow (load, train, test, adjust) in DIGITS
  • Programmatic ways to test performance and guide performance improvement

Upon completion, you will be able to classify both image and image-like data using deep learning.

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Deep Learning for Genomics using DragoNN with Keras and Theano

Level: Advanced | Prerequisites: Basic understanding of genomics
Industry: Healthcare | Frameworks: Theano

Learn to interpret deep learning models to discover predictive genome sequence patterns. Use the DragoNN toolkit on simulated and real regulatory genomic data to:

  • Demystify popular DragoNN (Deep Regulatory Genomics Neural Network) architectures
  • Explore guidelines for modeling and interpreting regulatory sequence using DragoNN models
  • Identify when DragoNN is a good choice for a learning problem in genomics and high-performance models

Upon completion, you will be able to use the discovery of predictive genome sequence patterns to hopefully gain new biological insights.

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Image Classification with TensorFlow: Radiomics - 1p19q Chromosome Status Classification

Level: Intermediate | Prerequisites: Basic understanding of convolutional neural networks and genomics
Industry: Healthcare | Frameworks: Caffe

Thanks to work being performed at Mayo Clinic, using deep learning techniques to detect Radiomics from MRI imaging has led to more effective treatments and better health outcomes for patients with brain tumors. Learn to detect the 1p19q co-deletion biomarker by:

  • Designing and training Convolutional Neural Networks (CNNs)
  • Using Imaging Genomics (Radiomics) to create biomarkers that identify the genomics of a disease without the use of an invasive biopsy
  • Exploring the Radiogenomics work being done at the Mayo Clinic

Upon completion, you will have unique insight into the novelty and promising results of utilizing deep learning to predict Radiomics.

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Medical Image Analysis with R and MXNet

Level: Intermediate | Prerequisites: Image Classification Using DIGITS
Industry: Healthcare | Frameworks: MXNet

Convolutional neural networks (CNNs) can be applied to medical image analysis to infer patient status from non-visible images. Train a CNN to infer the volume of the left ventricle of the human heart from time-series MRI data and learn to:

  • Extend a canonical 2D CNN to more complex data
  • Use the framework MXNet through the standard Python API and through R
  • Process high dimensionality imagery that may be volumetric and have a temporal component

Upon completion, you will know how to use CNNs for non-visible images.

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Medical Image Segmentation with DIGITS

Level: Beginner | Prerequisites: None
Industry: Healthcare | Frameworks: Caffe

Image (or semantic) segmentation is the task of placing each pixel of an image into a specific class. In this lab, you will segment MRI images to measure parts of the heart by:

  • Extend Caffe with custom Python layers
  • Implementing the process of transfer learning
  • Creating fully Convolutional Neural Networks from popular image classification networks

Upon completion, you will be able to set up most computer vision workflows using deep learning.

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Modeling Time Series Data with Recurrent Neural Networks in Keras

Level: Intermediate | Prerequisites: Some experience training CNNs
Industry: Healthcare | Frameworks: Theano

Recurrent Neural Networks (RNNs) allow models to classify or forecast time-series data, like natural language, markets, and in the case of this lab, a patient’s health over time. You will:

  • Create training and testing datasets using electronic health records in HDF5 (hierarchical data format version five)
  • Prepare datasets for use with recurrent neural networks (RNNs), which allows modeling of very complex data sequences
  • Construct a long-short term memory model (LSTM), a specific RNN architecture, using the Keras library running on top of Theano to evaluate model performance against baseline data

Upon completion, you will be able to model time-series data using Recurrent Neural Networks.

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Deep Learning Workflows with TensorFlow and MXNet and NVIDIA-Docker

Level: Beginner | Prerequisites: Bash terminal familiarity
Industry: All | Frameworks: TensorFlow

The NVIDIA-Docker plugin makes it possible to containerize production-grade deep learning workflows using GPUs. Learn to considerably reduce host configuration and administration by:

  • Learning to work with Docker images and manage the container lifestyle
  • Accessing images on the public Docker image registry DockerHub for maximum reuse in creating composable lightweight containers
  • Training neural networks using both TensorFlow and MXNet frameworks

Upon completion, you will be able to containerize and distribute pre-configured images for deep learning.

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Want more? Visit www.nvidia.com/DLI for upcoming DLI workshops, educational deep learning resources, and more.

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