Generative AI Multimodal

Skills covered in this course:

• Define generative AI and explain how it works. ​
• Describe various generative AI applications. ​
• Explain the challenges and opportunities in generative AI.

You can take one of these courses:

Skills covered in these courses:

• Enhance datasets through data augmentation to improve model accuracy.

• Understand the fundamental techniques and tools required to train a deep learning model.

• Gain experience with common deep learning data types and model architectures. 
• Leverage transfer learning between models to achieve efficient results with less data and computation. 
• Take on your own project with a modern deep learning framework.

You can take one of these courses:

Skills covered in these courses:

• Implement GPU-accelerated data preparation and feature extraction using cuDF and Apache Arrow data frames​.
• Apply a broad spectrum of GPU-accelerated machine learning tasks using XGBoost and a variety of cuML algorithms.​
• Execute GPU-accelerated graph analysis with cuGraph, achieving massive-scale analytics in small amounts of time.​
• Rapidly achieve massive-scale graph analytics using cuGraph routines.

Skills covered in this course:

• Customize and deploy automatic speech recognition (ASR) and test-to-speech (TTS) models on NVIDIA® Riva.​
• Build and deploy an end-to-end conversational AI pipeline, including ASR, natural language processing (NLP), and TTS models, on Riva.​
• Deploy a production-level conversational AI application with a Helm chart for scaling in Kubernetes clusters.

• Customize and deploy ASR and TTS models on Riva.​
• Build and deploy an end-to-end conversational AI pipeline, including ASR, NLP, and TTS models, on Riva.

Skills covered in this course:

• Extract meaningful insights from the provided dataset using pandas DataFrame.​
• Apply transfer learning to a deep learning classification model.​
• Fine-tune the deep learning model and set up evaluation metrics.​
• Deploy and measure model performance.​
• Experiment with various inference configurations to optimize model performance.

Skills covered in this course:

• Prepare data and build, train, and evaluate models using XGBoost, autoencoders, and generative adversarial networks (GANs).​
• Detect anomalies in datasets with both labeled and unlabeled data​.
• Classify anomalies into multiple categories regardless of whether the original data was labeled.

Skills covered in this course:

• Use time-series data to predict outcomes with XGBoost-based machine learning classification models.​
• Use anomaly detection with time-series autoencoders to predict failures when limited failure-example data is available.

Skills covered in this course:

• Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data.  
• Leverage pretrained, modern LLMs to solve various natural language processing (NLP) tasks such as token classification, text classification, summarization, and question-answering.

• Learn to describe how transformers are used as the basic building blocks of modern LLMs for NLP applications​.
• Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data.

• Leverage pretrained, modern LLMs to solve various NLP tasks such as token classification, text classification, summarization, and question-answering.

• Understand how transformer-based LLMs can be used to manipulate, analyze, and generate text-based data.

Skills covered in this course:

• Improve the quality of generated images with the denoising diffusion process.​
• Control the image output with context embeddings. Test and refine the context embeddings to achieve the desired image output, which necessitate experimental approaches to optimize performance.

• Generate images from English text-prompts using contrastive language-image pretraining (CLIP).

• Generate images from pure noise.​
• Generate images from English text prompts using CLIP.

• Understand content authenticity and how to build trustworthy models.

Skills covered in this course:

• Find, pull in, and experiment with the Hugging Face model repository and the associated transformers API.​
• Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.​
• Use decoder models to generate sequences like code, unbounded answers, and conversations.

• Find, pull in, and experiment with the Hugging Face model repository and the associated transformers API​.​
• Use state management and composition techniques to guide LLMs for safe, effective, and accurate conversation.

• Use state management and composition techniques to guide LLMs for safe, effective, and accurate conversation.

Skills covered in this course:

• Know how to apply fine-tuning techniques.
• Understand how to effectively integrate and interpret diverse data types within a single model framework.

• Use a single pretrained model to perform multiple custom tasks involving different types of data (e.g., text, images, audio).

• Leverage the NVIDIA NeMo™ framework to customize models like GPT, LLaMA-2, and Falcon with ease.

• Assess the performance of fine-tuned models.

• Use fine-tuning to perform optimization to enhance a model's accuracy, efficiency, or effectiveness for specific tasks.

Skills covered in this course:

• Deploy neural networks from a variety of frameworks onto a live NVIDIA Triton™ server.​
• Measure GPU usage and other metrics with Prometheus.​
• Send asynchronous requests to maximize throughput.

• Measure GPU usage and other metrics with Prometheus​.
• Send asynchronous requests to maximize throughput.