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NVIDIA Certified Trainings

Astra Intellectus Tripartite MoU signing with APU and Vero AI 

Astra Intellectus delivering [Fundamentals of Deep Learning], first ever NVIDIA Deep Learning Intitute Certified Training in Malaysia, and in Asia Pacific University (APU).

// about service

Certified Courses from NVIDIA DEEP LEARNING INSTITUTE

The Deep Learning Institute (DLI) is NVIDIA’s global platform for AI education and training—developed by the company at the forefront of modern AI innovation. It delivers high-quality, GPU-accelerated learning in deep learning, data science, large language models, and hands-on virtual labs.

Designed for university students, researchers, and industry professionals alike, DLI courses reflect the latest advancements and real-world practices shaping today’s AI landscape—equipping learners with knowledge that is directly aligned with how AI is actually built and deployed.

01
Machine Learning

Support and Evolution

02
Artificial Intelligence

Support and Evolution

03
Augmented Reality

Support and Evolution

//Course: FUNDAMENTALS OF DEEP LEARNING

Certification issued directly by NVIDIA to you
(sample)

// ABOUT THE COURSE

NOT JUST FOR IT PROFESSIONALS

PURPOSE-BUILT FOR NON-IT DECISION-MAKERS & LEADERS

We also strongly recommend  <Fundamentals of Deep Learning> for business leaders who need to understand how data is used in today’s AI to train and learn.
Gain clarity on what AI can and cannot do, and begin to grasp how it can enhance—or even transform—your business model. 
Understand where real competitive advantage lies, and how to approach AI with informed, realistic expectations. 
Equip yourself with the insight to align your company’s future direction with AI—without being misled by hype.

// COURSE - Fundamentals of Deep Learning

LEARNING OBJECTIVES

  • Learn the fundamental techniques and tools required to train a deep learning model
  • Gain experience with common deep learning data types and model architectures
  • Enhance datasets through data augmentation to improve model accuracy
  • Leverage transfer learning between models to achieve efficient results with less data and computation
  • Build confidence to take on your own project with a modern deep learning framework
// Course: Fundamentals of deep learning

FURTHER COURSE DETAILS

  • Duration: 8 hours of training with 6-month access to course materials and labs after completion (lab resources have usage limits).
  • Format: Live instructor-led sessions. Each participant will have access to fully configured cloud-based GPU-accelerated workstations for hands-on practice.
  • Prerequisites: Beginner
  • Techonologies: Covered PyTorch, Pandas, Convolutional Neural Networks (CNNs), Data Augmentation, Transfer Learning, Natural Language Processing.
  • Assessment: Format Skill-oriented coding evaluations to test the ability to train deep learning models to achieve high accuracy.
  • Certification: Participants will receive an NVIDIA DLI Training Certificate upon successfully completing the course and assessments, providing proof of skills for career advancement.
  • Language: English
  • Hardware Requirements: A laptop or desktop capable of running the latest web browser is required. Participants will be provided with dedicated access to fully configured cloud-based accelerated workstations.

*** Course must be led by NVIDIA-certified trainers.

//other courses to choose from

more NVIDIA CERTIFIED COURSES

For Non-IT Specialists and Business Professionals: Sientists, Artists, Entrepreneurs, Lawyers, and Marketing professionals.

RM2,XXX

NVIDIA Isaac for
Accelerated Robotics

Accelerate your robotics innovation with this hands-on workshop focused on simulation-first development, AI-powered perception, and synthetic data generation using NVIDIA Isaac.

Unlock the power of simulation-driven robotics with NVIDIA Isaac. This hands-on, full-day workshop is designed for robotics engineers, applied researchers, and R&D teams focused on building, training, and simulating robots in software. By the end of this workshop, attendees will have hands-on experience building robot simulations and integrating synthetic data pipelines to build scalable, simulation-first workflows.

*** Course must be led by NVIDIA-certified trainers.
Get Quote

RM2,XXX

Building Transformer-Based Natural Language Processing Applications

Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks Learn how to apply and fine-tune a Transformer-based Deep Learning model to Natural Language Processing (NLP) tasks.

In this course, you'll: · Construct a Transformer neural network in PyTorch · Build a named-entity recognition (NER) application with BERT · Deploy the NER application with ONNX and TensorRT to a Triton inference server Upon completion, you’ll be proficient in task-agnostic applications of Transformer-based models.

*** Course must be led by NVIDIA-certified trainers.
Get Quote

RM2,XXX

Generative AI with
Diffusion Models

Thanks to improvements in computing power and scientific theory, generative AI is more accessible than ever before. Generative AI plays a significant role across industries due to its numerous applications, such as creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more. In this course, learnes will take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines

*** Course must be led by NVIDIA-certified trainers.
Get Quote

RM2,XXX

Building AI Agents with Multimodal Models

Just like how humans have multiple senses to perceive the world around them, computers have a variety of sensors to help perceive the human world. In the health industry, computed tomography (CT) scans provide a 3D representation used to detect potentially dangerous abnormalities. In the robotics industry, lidars are used to help robots see depth and navigate the complex topology around them. In this course, learners will develop neural network based multimodal models that can understand many different data types by exploring different fusion techniques.

*** Course must be led by NVIDIA-certified trainers.
Get Quote

RM2,XXX

NVIDIA Isaac for Accelerated Robotics

Unlock the power of simulation-driven robotics with NVIDIA Isaac. This hands-on, full-day workshop is designed for robotics engineers, applied researchers, and R&D teams focused on building, training, and simulating robots in software. By the end of this workshop, attendees will have hands-on experience building robot simulations and integrating synthetic data pipelines to build scalable, simulation-first workflows.
MORE INFO

Generative AI & Large Language Model Series

  • Deep learning foundations—Theory and practical introduction
  • Build natural language processing applications based on Transformer
  • Build generative AI applications using expanded models
  • Build large language model applications
CONTACT FOR QUOTE

Accelerated Computing Series

  • Accelerated computing basics: CUDA C/C++
  • Accelerated computing basics: CUDA Python
  • Advanced ccelerated computing: GPU cceleration CUDA C++ applications
  • Advanced accelerated computing: Scaling CUDA C++ applications to multi-node
CONTACT FOR QUOTE

Accelerated Computing Series

  • Accelerated computing basics: CUDA C/C++
  • Accelerated computing basics: CUDA Python
  • Advanced ccelerated computing: GPU cceleration CUDA C++ applications
  • Advanced accelerated computing: Scaling CUDA C++ applications to multi-node
CONTACT FOR QUOTE

Generative AI & Large Language Model Series

  • Deep learning foundations—Theory and practical introduction
  • Build natural language processing applications based on Transformer
  • Build generative AI applications using expanded models
  • Build large language model applications
CONTACT FOR QUOTE

Accelerated Computing Series

  • Accelerated computing basics: CUDA C/C++
  • Accelerated computing basics: CUDA Python
  • Advanced ccelerated computing: GPU cceleration CUDA C++ applications
  • Advanced accelerated computing: Scaling CUDA C++ applications to multi-node
CONTACT FOR QUOTE

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