Generative AI LLMs

You can take one of these courses:

Skills covered in these courses:

• 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.

• Build confidence to take on your own project with a modern deep learning framework.
• Leverage transfer learning between models to achieve efficient results with less data and computation.

• Enhance datasets through data augmentation to improve model accuracy.

You can take one of these courses:

Skills covered in these courses:

• Utilize a wide variety of machine learning algorithms, including XGBoost, for different data science problems.
• Learn and apply powerful graph algorithms to analyze complex networks with NetworkX and cuGraph.

Understand GPU-accelerated data manipulation:​

• Ingest and prepare several datasets (some larger-than-memory) for use in multiple machine learning exercises. 
• Read data directly to single and multiple GPUs with cuDF and Dask cuDF. 
• Prepare information for machine learning tasks on the GPU with cuDF. 
• Apply several essential machine learning techniques to prepare data. 
• Use supervised and unsupervised GPU-accelerated algorithms with cuML. 
• Train XGBoost models with Dask on multiple GPUs. 
• Create and analyze graph data on the GPU with cuGraph. 
• Use NVIDIA RAPIDS™ to integrate multiple massive datasets and perform analysis. 
• 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.

• Learn and apply powerful graph algorithms to analyze complex networks with NetworkX and cuGraph.

• Deploy machine learning models on a Triton Inference Server to deliver optimal performance.

You can take one of these courses:

Skills covered in these courses:

• Learn to describe how transformers are used as the basic building blocks of modern LLMs for natural language processing (NLP) applications.  
• Learn how self-supervision improves upon the transformer architecture in BERT, Megatron, and other LLM variants for superior NLP results.

• Implement transformer-based models for different NLP applications.
• Develop solutions for text classification, NER, author attribution, and question answering using LLM
• Manage inference challenges and deploy refined models for live applications.

• Experiment with transformer-based models for various NLP tasks.
• Test and compare model performance on question answering tasks.
• Leverage pretrained, modern LLMs to solve various NLP tasks such as token classification, text classification,  summarization, and question-answering.

• Use transformer-based models for text classification.
• Apply LLMs for named-entity recognition (NER).
• Utilize transformer models for author attribution.
• Learn how to leverage pretrained, modern LLM models to solve multiple NLP tasks such as text classification, named-entity recognition (NER), and question answering.

Skills covered in this course:

• Understand how to apply iterative prompt engineering best practices to create LLM-based applications for various language-related tasks.

• Learn to be proficient in using LangChain to organize and compose LLM workflows.

• Write application code to harness LLMs for generative tasks, document analysis, chatbot applications, and more.

• Learn to be proficient in using LangChain to organize and compose LLM workflows.

Skills covered in this course:

• Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.
• Work with conditioned decoder-style models to take in and generate interesting data formats, styles, and modalities.

• Explore the use of LangChain and LangGraph for orchestrating data pipelines and environment-enabled agents.

• Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.

• Find, pull in, and experiment with the Hugging Face model repository and the associated transformers API.

• Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.