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

Fine-Tuning BERT for Custom NLP Tasks

Parameter-Efficient Fine-Tuning: Guide to LoRA and QLoRA for LLMs

Evaluating Large Language Models: Practical Assessment Strategies

Scaling Laws for Large Language Models: Core Concepts and Trade-Offs

Transfer Learning in PyTorch: Fine-Tuning MobileNetV3

Interactive LLM Chatbots with Gradio

Building Interactive ML Interfaces with Gradio Sliders

Automated Model Building and Hyperparameter Tuning with SageMaker

LSTM Modeling with JAX and Flax

Fine-Tuning Foundation Models: Guide to Customizing LLMs

Customizing LLMs: Choosing Between RAG, ICL, and Fine-Tuning

Model Scaling for Concatenation-Based Models in YOLOv7

Efficient LLM Inference: Infrastructure and Serving Optimization

Building LSTM Recurrent Neural Networks with JAX and Flax

Parameter-Efficient Fine-Tuning: LoRA for Large Language Models

Generating Synthetic Data for LLM Testing and Edge Cases

Practical LoRA: Fine-Tune Large Language Models on Custom Data

Choosing Fine-Tuning or RAG for Large Language Models

Evaluating LLMs and AI: Metrics, Benchmarks, and Best Practices

Resilient LLM Training with SageMaker HyperPod

SageMaker AI: Machine Learning Model Training and Optimization

Foundations of RLHF: Building an AI Alignment Loop

Fine-Tuning PyTorch Models: Overcoming Underfitting and Overfitting

Deep Matching for Entity Resolution with BERT