This no-code course offers a comprehensive introduction to Generative AI, focusing on its concepts, applications, and the challenges and opportunities it presents. Learn about neural networks' role in generating new content, explore real-world applications, and gain insights into the field's future.

Learning Objectives:

• Understand Generative AI and its underlying mechanisms.
• Recognize various applications of Generative AI.
• Discuss challenges and opportunities in this field.

Course Outline:

• Generative AI definition and functionality.
• Applications of Generative AI in different contexts.
• Examination of challenges and opportunities within Generative AI.

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This notebook explores the biological and psychological inspirations to the world's first neural networks.

Learning Objectives:

The goals of this exercise include:

• 
  Exploring how neural networks use data to learn.
  
• Understanding the math behind a neuron.
  

Topics Covered:

• AI Data
  
• Neurons
  
• TensorFlow 2

Course Outline:

• Data
  
• Building a Neuron
• Initiate Training
  
• Evaluating the Model

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Unlock the potential of AI with the NVIDIA Jetson Nano Developer Kit! This course guides you through building deep learning projects on your Jetson Nano using Jupyter iPython notebooks. Create image classification, object detection, segmentation, and speech processing applications.

Exclusive note: This course is specifically tailored to support the NVIDIA Jetson Nano Developer Kit, offering a unique learning experience that is not available for the NVIDIA Jetson Orin Nano Developer Kit.

Required Hardware:

• NVIDIA Jetson Nano Developer Kit
  
• High-performance 32GB microSD card
  
• 5V 4A power supply with 2.1mm DC barrel connector
  2-pin jumper
  
• Logitech C270 USB Webcam
  USB cable (Micro-B To Type-A)
  

Additional Computer Requirements:

• Internet-connected computer for flashing microSD card
  
• Available USBA port (adapter needed for USBC ports)
  

Learning Objectives:

• Set up Jetson Nano and camera
  Collect and annotate image data
  Train neural networks and create models
  Run inference on Jetson Nano with custom models
  Upon completion, you'll have the skills to develop deep-learning classification and regression models with the Jetson Nano. 

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** UPDATE **

I haven't found the Jetson Neno Kit available anywhere!

Seize the opportunity to explore the world of advanced Large Language Models (LLMs) with this exclusive limited-time free course! Learn practical deployment strategies and design LLM systems that interact efficiently with users through internal reasoning, dialog management, and effective tooling. Dive into topics such as inference interfaces, pipeline design, dialog states, semantic similarity, and more to develop your own cutting-edge LLM applications.

Learning Objectives:

• Compose LLM systems leveraging internal and external reasoning components
• Design dialog management systems for information structuring
• Implement embedding models for content retrieval and dialog guard railing
• Develop a RAG agent for answering questions without fine-tuning

Course Outline:

• Introduction and environment setup
• LLM inference interfaces and microservices
• Designing LLM pipelines using LangChain, Gradio, and LangServe
• Dialog state management and knowledge extraction
• Working with long-form documents
• Embeddings for semantic similarity and guard railing
• Vector stores for efficient document retrieval
• Evaluation, assessment, and certification

Upon completion, participants will possess the knowledge and skills to develop and deploy their own LLM applications confidently.

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** DISCLAIMER **

This course is not going to remain free for all times.

Discover the power of NVIDIA RAPIDS to accelerate your data science workloads across industries. In this course, you'll learn how to harness the efficiency of GPU acceleration with zero code changes, speeding up data processing and increasing productivity. Gain hands-on experience in GPU-accelerated workflows and witness a significant reduction in processing time.

Learning Objectives:

• Understand the benefits of a unified CPU-GPU workflow for data science tasks
• Learn to GPU-accelerate data processing and machine learning workflows without code changes
• Experience the remarkable reduction in processing time with GPU acceleration

Course Outline:

• Introduction to a unified CPU-GPU workflow for data science
• GPU-accelerating various data processing and machine learning workflows
• Demonstrating the efficiency of GPU-accelerated workflows through hands-on exercises

Upon completion, you'll have the knowledge and skills to leverage NVIDIA RAPIDS and significantly speed up your data science workflows.

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Learn about AI, machine learning, deep learning, GPU architecture, deep learning frameworks, and deploying AI workloads. Understand requirements for multi-system AI clusters and infrastructure planning.

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