Parameter-Efficient Fine-Tuning Courses and Certifications

Association for Computing Machinery (ACM)

When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications - Lecture 1

Explore parameter-efficient fine-tuning techniques for multi-task medical applications using Mixture of Experts and Large Language Models.

• YouTube
• 14 minutes
• On-Demand
• Free Video

USENIX

mTuner - Accelerating Parameter-Efficient Fine-Tuning on Multi-GPU Servers with Elastic Tensor

Discover mTuner, a system using Elastic Tensor abstraction to optimize memory utilization and accelerate parameter-efficient fine-tuning of large language models on multi-GPU servers.

• YouTube
• 16 minutes
• On-Demand
• Free Video

Association for Computing Machinery (ACM)

IISAN: Efficiently Adapting Multimodal Representation for Sequential Recommendation - M2.6

Explore efficient multimodal representation adaptation for sequential recommendations using decoupled PEFT in this innovative research presentation.

• YouTube
• 14 minutes
• On-Demand
• Free Video

Discover AI

Parameter-Efficient Fine-Tuning with LoRA - Optimizing LLMs for Local GPU Training

Master PEFT LoRA techniques to efficiently fine-tune large language models on local GPUs through low-rank adaptation, reducing memory requirements while maintaining model performance.

• YouTube
• 41 minutes
• On-Demand
• Free Video

Microcredential

Generative AI

118 ratings at Udacity

Unlock the power of Generative AI through hands-on projects covering LLMs, computer vision, chatbots, and real-world applications with PyTorch and Hugging Face.

• Udacity
• 56 hours
• On-Demand
• Paid Course

Generative AI Fundamentals

9 ratings at Udacity

Master generative AI principles, deep learning fundamentals, and foundation model adaptation using PyTorch and Hugging Face with hands-on PEFT implementation.

• Udacity
• 21 hours
• On-Demand
• Paid Course

Fine-Tuning AI Agents with Reinforcement Learning

Master advanced techniques to transform static LLMs into adaptive AI agents using reinforcement learning, supervised fine-tuning, and alignment methods for autonomous decision-making.

• Udacity
• 15 hours
• On-Demand
• Paid Course

Discover AI

Parameter Efficient Fine-Tuning with Multiple LoRA Adapters for Large Language Models

Dive into parameter efficient fine-tuning with LoRA adapters, exploring matrix factorization, SVD, and advanced configurations for optimizing large language models.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Generative AI - Adapting LLMs with Parameter-Efficient Fine-Tuning - Lecture 10

Master parameter-efficient fine-tuning techniques for adapting large language models, covering GPT versions, training data nuances, and instruction tuning methods.

• YouTube
• 1 hour 18 minutes
• On-Demand
• Free Video

Toronto Machine Learning Series (TMLS)

Prompt Optimization and Parameter Efficient Fine Tuning for Large Language Models

Explore state-of-the-art prompt optimization techniques and parameter efficient fine-tuning methods for large language models to enhance performance on various tasks.

• YouTube
• 28 minutes
• On-Demand
• Free Video

CodeEmporium

LoRA - Low Rank Adaptation for Parameter Efficient Fine-Tuning

Dive into parameter-efficient fine-tuning with LoRA, exploring low-rank matrices, adapters, and techniques to optimize neural network storage and performance while maintaining accuracy.

• YouTube
• 29 minutes
• On-Demand
• Free Video

HOTI - Hot Interconnects Symposium

Characterizing Communication in Distributed Parameter-Efficient Fine-Tuning for LLMs

Explore communication patterns and optimization techniques in distributed parameter-efficient fine-tuning for Large Language Models, focusing on network performance and scalability challenges.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Generative AI Engineering and Fine-Tuning Transformers

107 ratings at Coursera

Explore transformers, fine-tuning, and PEFT techniques for LLMs. Gain hands-on experience with Hugging Face and PyTorch to develop job-ready generative AI engineering skills.

• Coursera
• 8 hours 2 minutes
• On-Demand
• Paid Course

Intro to Federated Learning

12 ratings at Coursera

Discover how to train AI models across distributed devices without sharing data using Flower framework, exploring privacy-preserving techniques and LLM fine-tuning methods.

• Coursera
• 1 hour 30 minutes
• On-Demand
• Paid Course

Whizlabs

NVIDIA: Large Language Models and Generative AI Deployment

Master the deployment and optimization of Large Language Models through hands-on training in model fundamentals, GPU-accelerated training techniques, PEFT methods, and ONNX-based monitoring strategies.

• Coursera
• 4 hours 24 minutes
• On-Demand
• Paid Course