PickAClass · AI, Tech & Engineering · Generative AI & Deep Learning

Fine-Tuning Language Models

Go beyond prompting by learning to fine-tune pre-trained language models on custom datasets. Adapt models like GPT, Llama, and BERT for specific domains and tasks to improve performance and accuracy.

Fine-Tuning Language Models

Using Default Hugging Face Models for NLP and Vision Tasks

Fine-Tuning Foundational Models with AWS Bedrock

Introduction to Transfer Learning and Model Compression for ML Deployment

Integrating Hugging Face Foundation Models in Go

AWS SageMaker: Built-In Algorithms and Custom Model Training

Fine-Tuning LLMs with LoRA: Practical Instruction Tuning

Working with OpenAI API Models: A Beginner's Guide

Large Language Models: Navigating Risks, Bias, and Security

Introduction to Vision-Language Models and Multimodal AI

AWS Generative AI: Choosing Fine-Tuning or RAG for Exams and Architecture

SageMaker Training Approaches for ML Model Optimization

Business Strategy for Custom LLM Applications

Entity and Relationship Extraction with LLMs and Python

Evaluating LLM Fine-Tuning: Limitations, RAG, and Alternatives

Hyperparameter Tuning for XGBoost with tidymodels in R

Customizing AI Models with Bedrock: Fine-Tuning and RAG Strategies

LLM Fine-Tuning and Alignment: SFT, RLHF, and DPO

Evaluating Language Models: BLEU, ROUGE, and METEOR Metrics

Choosing and Fine-Tuning Foundation Models for AI Applications

Fine-Tuning and Hyperparameter Tuning for YOLO Models

BERT Fine-Tuning for Extractive Summarization

Cross-Lingual Language Models (XLM) for Multilingual NLP

LLM Decision Guide: Choosing Between Prompting, RAG, and Fine-Tuning

Inside the LLM Output Layer: From Vectors to Next-Token Prediction