Nvidia AI Agent Architect Program -
2-Year Curriculum

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Year 1: Foundations of AI and Machine Learning

Semester 1: Introduction to AI and Machine Learning

• AI Fundamentals

  • Overview of Artificial Intelligence

  • AI applications and use cases in real-world industries

• Python for AI

  • Python programming basics for AI applications

  • Data manipulation with NumPy and Pandas

• Introduction to Machine Learning

  • Supervised vs. unsupervised learning

  • Regression, classification, and clustering algorithms

• Nvidia Deep Learning Institute (DLI) Tools

  • Getting started with Nvidia GPU-accelerated platforms

  • Intro to Nvidia CUDA programming

Semester 2: Deep Learning Essentials

• Neural Networks and Deep Learning

  • Basics of neural networks, activation functions, and backpropagation

  • Deep learning with TensorFlow and PyTorch

• Convolutional Neural Networks (CNNs)

  • Image processing and computer vision applications

  • Transfer learning with pre-trained models

• Reinforcement Learning (RL)

  • Fundamentals of RL and Q-learning

  • Exploration and exploitation in decision-making systems

• Nvidia Hardware Acceleration

  • Leveraging Nvidia GPUs for deep learning training

  • Introduction to Nvidia TensorRT for inference acceleration

Year 2: Advanced AI Agent Architectures and Deployment

Semester 3: Building AI Agents

• Advanced Reinforcement Learning

  • Deep Q-Networks (DQN) and policy gradient methods

  • Actor-critic methods and applications

• Natural Language Processing (NLP) for AI Agents

  • Text generation using RNNs and Transformers

  • BERT and GPT architectures for NLP-based agents

• AI Ethics and Bias Mitigation

  • Responsible AI design and ethical considerations

  • Bias in AI algorithms and fairness in decision-making

• Nvidia Isaac Platform for Robotics

  • Designing AI agents for robotics and automation

  • Isaac SDK for simulation, training, and deployment of AI-driven robots

Semester 4: AI Agent Architectures and Deployment Strategies

• Autonomous Systems and Multi-Agent Environments

  • Designing agents for autonomous vehicles and drones

  • Multi-agent systems, communication, and coordination

• AI in Edge Computing

  • Deploying AI agents on edge devices with Nvidia Jetson

  • Optimization for low-latency environments

• Advanced AI Architectures

  • GANs for creative AI agents

  • Designing scalable AI architectures using Nvidia tools (NGC, Nvidia Omniverse)

• Capstone Project: AI Agent Deployment

  • Build and deploy a fully functional AI agent architecture for a specific Nvidia-related application (e.g., autonomous systems, healthcare, robotics) using deep learning and reinforcement learning techniques.

  • Final project review with Nvidia engineers.