Today,, the NVIDIA Deep Learning Institute (DLI) launches a series of online courses and instructor-led remote workshops for developers, data scientists, students, and researchers, spanning topics such as deep learning for multi-GPUs to accelerated data science.
The NVIDIA Deep Learning Institute offers hands-on training in AI, accelerated computing, and accelerated data science. offering practical experience powered by GPUs in the cloud. Course participants earn a certificate of competency to support professional growth.
Get started with DLI through self-paced, online training for individuals, instructor-led workshops for teams, and downloadable course materials for university educators.
Online Courses
For self-learners and small teams, we recommend self-paced, online training through DLI and online courses through our partners. With DLI, you’ll have access to a fully configured, GPU-accelerated server in the cloud, gain practical skills for your work, and have the opportunity to earn a certificate of subject matter competency.
Deep Learning Fundamentals
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.
- Prerequisites: Familiarity with basic programming fundamentals such as functions and variables
- Technologies: Caffe, DIGITS
- Duration: 8 hours
- Price: $90 (excludes tax, if applicable)
Getting Started on AI with Jetson Nano
- Explore how to build a deep learning classification project with computer vision models using an NVIDIA® Jetson™ Nano Developer Kit.
- Prerequisites: Familiarity with Python (helpful, not required)
- Technologies: PyTorch, Jetson Nano
- Duration: 8 hours
- Price: Free
Optimisation and Deployment of TensorFlow models with TensorRT
- Learn how to optimize TensorFlow models to generate fast inference engines in the deployment stage.
- Prerequisites: Experience with TensorFlow and Python
- Technologies: TensorFlow, Python, NVIDIA TensorRT™ (TF-TRT)
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Deep Learning at scale with Horovod
- Learn how to scale deep learning training to multiple GPUs with Horovod, the open-source distributed training framework originally built by Uber and hosted by the LF AI Foundation.
- Prerequisites: Competency in Python and experience training deep learning models in Python
- Technologies: Horovod, TensorFlow, Keras, Python
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Image segmentation with TensorFlow
- Learn to combine computer vision and natural language processing to describe scenes using deep learning.
- Prerequisites: Basic experience training neural networks
- Technologies: TensorFlow
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Modelling TimeSeries Data with Recurring Neural Networks in Keras
- Explore how to classify and forecast time-series data, such as modeling a patient’s health over time, using recurrent neural networks (RNNs).
- Prerequisites: Basic experience with deep learning
- Technologies: Keras
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Deep Learning for Digital Content Creation
Image Style Transfer with Torch
- Learn how to transfer the look and feel of one image to another image by extracting distinct visual features using convolutional neural networks (CNNs).
- Prerequisites: Experience with CNNs
- Technologies: Torch, CNNs
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Image Super Resolution using Autoencoders
- Leverage the power of a neural network with autoencoders to create high-quality images from low-quality source images.
- Prerequisites: Experience with CNNs
- Technologies: Keras, CNNs
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Deep Learning for Intelligent Video Analytics
AI Workflows for Intelligent Video Analytics with DeepStream
- Learn how to build hardware-accelerated applications for intelligent video analytics (IVA) with DeepStream and deploy them at scale to transform video streams into insights.
- Prerequisites: Experience with C++ and Gstreamer
- Technologies: DeepStream3, C++, Gstreamer
- Duration: 2 hours
- Price: $30 (excludes tax, if applicable)
Getting Started with DeepStream for Video Analytics on Jetson Nano
- Learn how to build DeepStream applications to annotate video streams using object detection and classification networks.
- Prerequisites: Basic familiarity with C
- Technologies: DeepStream, TensorRT, Jetson Nano
- Duration: 8 hours; Self-paced
- Price: Free
Instructor-led workshops
For large teams or self-learners interested in training, we recommend full-day workshops led by DLI-certified instructors. You can request a full-day workshop onsite or remote delivery for your team. With DLI, you’ll have access to a fully configured, GPU-accelerated server in the cloud, gain practical skills for your work, and have the opportunity to earn a certificate of subject matter competency.
Deep Learning fundamentals
Fundamentals of Deep Learning for Multi-GPUs
- Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently.
- In this course, you will learn how to scale deep learning training to multiple GPUs. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible. This course will teach you how to use multiple GPUs to train neural networks. You’ll learn:
- Approaches to multi-GPU training
- Algorithmic and engineering challenges to large-scale training
- Key techniques used to overcome the challenges mentioned above
- Upon completion, you’ll be able to effectively parallelize training of deep neural networks using Horovod.
- Prerequisites: Competency in the Python programming language and experience training deep learning models in Python.
- Technologies: Python, Tensorflow.
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.
- Prerequisites: Familiarity with basic programming fundamentals such as functions and variables
- Technologies: Caffe, DIGITS
Fundamentals of Deep Learning for Multiple Data Types
- This workshop explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.
- Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:
- Implementing deep learning workflows like image segmentation and text generation
- Comparing and contrasting data types, workflows, and frameworks
- Combining computer vision and natural language processing
- Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.
- Prerequisites: Familiarity with basic Python (functions and variables); prior experience training neural networks.
- Technologies: TensorFlow
Fundamentals of Deep Learning for Natural Language Processing
- Learn the latest deep learning techniques to understand textual input using natural language processing (NLP). You’ll learn how to:
- Convert text to machine-understandable representations and classical approaches
- Implement distributed representations (embeddings) and understand their properties
- Train machine translators from one language to another
- Upon completion, you’ll be proficient in NLP using embeddings in similar applications.
- Prerequisites: Basic experience with neural networks and Python programming; familiarity with linguistics
- Technologies: TensorFlow, Keras
Deep Learning for Digital Content Creation Using Autoencoders
- Explore the latest techniques for designing, training, and deploying neural networks for digital content creation. You’ll learn how to:
- Apply the architectural innovations and training techniques used to make arbitrary video style transfer
- Train your own denoiser for rendered images
- Upscale images with super resolution AI
- Upon completion, you’ll be able to start creating digital assets using deep learning approaches.
- Prerequisites: Basic familiarity with deep learning concepts such as convolutional neural networks (CNNs); experience with the Python programming language
- Technologies: Torch, TensorFlow
Besides these sessions and workshops there’s plenty more on offer, including several public sessions you can sign up to – click here for more information.