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NVIDIA DEEP LEARNING INSTITUTE

홈으로NVIDIA Deep Learning Institute

NVIDIA DEEP LEARNING INSTITUTE

한컴아카데미는 국내 최초의 NVIDIA DLI(Deep Learning Institute) 공인 교육센터로, Multi GPU, CUDA 등 NVIDIA의 핸즈온 딥러닝 실습 교육 과정을 제공합니다.
NVIDIA DLI는 개발자, 데이터 과학자 및 연구원에게 인공지능 및 가속화된 컴퓨팅을 사용하여 자율주행 자동차, 헬스케어, 재무 등 다양한 영역의 실제 문제를 해결하는 방법을 교육합니다.
딥러닝 과정에서는 최신 도구, 프레임 워크 및 기술을 사용하여 신경 네트워크를 학습하고, 최적화 및 배포하는 방법을 학습할 수 있습니다. 가속 컴퓨팅 강좌에서는 광범위한 응용 프로그램 영역에서 GPU 가속 컴퓨팅 응용 프로그램을 평가, 병렬화, 최적화 및 배포하는 방법을 배우게 됩니다.

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NVIDIA DLI 공인 강사

한컴아카데미는 까다로운 심사를 통과한 DLI 공인 강사를 보유하고 있습니다.

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NVIDIA DLI 교육 과정

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')

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