Fundamentals of Accelerated Computing With CUDA C/C++(6h)

• Accelerating Applications with CUDA C/C++ (120��)
• Managing Accelerated Application Memory with CUDA C/C++ (120��)
• Asynchronous Streaming and Visual Profiling for Accelerated Applications with CUDA C/C++ (120��)

Fundamentals of Accelerated Computing With CUDA Python(6h)

• Introduction to CUDA Python with Numba (120')
• Custom CUDA Kernels in Python with Numba(120')
• RNG, Multidimensional Grids, and Shared Memory for CUDA Python with Numba (120')

Accelerating CUDA�� C++ Applications With Multiple GPUs(6h)

• Using JupyterLab (15')
• Application Overview (15')
• Introduction to CUDA Streams (90')
• Copy/Compute Overlap with CUDA Streams (90')
• Multiple GPUs with CUDA C++ (60')
• Copy/Compute Overlap with Multiple GPUs (60')
• Course Assessment (30')

Fundamentals of DeepLearning(6h)

• The Mechanics of Deep Learning (120')
• Pre-trained Models and Recurrent Networks(120')
• Final Project: Object Classification (120')

Building AI-Based Cybersecurity Pipelines(6h)

• An Overview of the NVIDIA Morpheus AI Framework (30')
• Morpheus Pipeline Construction (45')
• Inference in Morpheus Pipelines (45')
• Case Study: AI-Based Machine Log Parsing at Splunk (30')
• Digital Fingerprinting Pipeline (45')
• Time Series Analysis (45')
• Case Study: Cybersecurity Flyaway Kit at Booz Allen Hamilton (30')
• Assessment 1: Test Your Understanding(45')
• Assessment 2: Practical Demonstration (45')

Model Parallelism: Building and Deploying Large Neural Networks(6h)

• Introduction to Training of Large Models (120��)
• Model Parallelism: Advanced Topics (120��)
• Inference of Large Models (120��)

Data Parallelism: How to Train Deep Learning Models on Multiple GPUs(6h)

• Stochastic Gradient Descent and the Effects of Batch Size (120')
• Training on Multiple GPUs with PyTorch Distributed Data Parallel (DDP) (120')
• Maintaining Model Accuracy when Scaling to Multiple GPUs (90')
• Workshop Assessment(30')