NVIDIA Deep Learning Institute (DLI) Training
As NVIDIA Service Delivery Partner – Education Services we offer the original NVIDIA Deep Learning Institute (DLI) training portfolio. NVIDIA DLI courses are available as public classroom training or as individual company training.
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NVIDIA DLI Training Programs
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be
cancelled and no refund will be issued, regardless of attendance.
If you have any questions please feel free to get in contact with our team who will be more than willing to help - contact form or call us at 0845 470 1000.
Customized NVIDIA Workshops
We are happy to tailor courses to suit your company's goals and requirements as well as participants' prior knowledge and skills. Simply let us know what you need.
Request individual company training
Why Choose NVIDIA Deep Learning Institute Training?
- Access workshops from anywhere with just your desktop/laptop computer and an internet connection. Each participant will have access to a fully configured, GPU-accelerated server in the cloud.
- Obtain hands-on experience with the most widely used, industry-standard software, tools, and frameworks.
- Learn to build deep learning and accelerated computing applications for industries, such as healthcare, robotics, manufacturing, accelerated computing, and more.
- Gain real-world expertise through content designed in collaboration with industry leaders, such as the Children’s Hospital of Los Angeles, Mayo Clinic, and PwC.
- Earn an NVIDIA Deep Learning Institute certificate to demonstrate your subject matter competency and support your career growth
NVIDIA courses by category
Accelerated Computing
- Fundamentals of Accelerated Computing with CUDA Python (FACCP)
- Accelerating CUDA C++ Applications with Multiple GPUs (ACCAMG)
- Fundamentals of Accelerated Computing with OpenACC (FACO)
- Fundamentals of Accelerated Computing with CUDA C/C++ (FACCC)
- Scaling CUDA C++ Applications to Multiple Nodes (SCCAMN)
Deep Learning
- Applications of AI for Anomaly Detection (AAAD)
- Data Parallelism: How to Train Deep Learning Models on Multiple GPUs (DPHTDLM)
- Fundamentals of Deep Learning (FDL)
- Building Conversational AI Applications (BCAA)
- Applications of AI for Predictive Maintenance (AAPM)
- Model Parallelism: Building and Deploying Large Neural Networks (MPBDLNN)
- Building Transformer-Based Natural Language Processing Applications (BNLPA)
- Computer Vision for Industrial Inspection (CVII)
- Building AI-Based Cybersecurity Pipelines (BABCP)