
Understand the difference between Narrow Task AI vs General Purpose AI vs Artificial General Intelligence (AGI)
https://code.visualstudio.com/download
Link to repository: danteachqe/LLMs: a comprehensive code repo for testing LLMs
Repo ->https://github.com/danteachqe/LLMs/tree/main/LLM/Data_Splitting
Repo -> https://github.com/danteachqe/LLMs/tree/main/LLM/Data_Splitting
https://gluebenchmark.com
Dateset : https://huggingface.co/datasets/nyu-mll/glue
Tasks: https://gluebenchmark.com/tasks
This comprehensive course delves into the essential practices, tools, and datasets for AI model benchmarking. Designed for AI practitioners, researchers, and developers, this course provides hands-on experience and practical insights into evaluating and comparing model performance across tasks like Natural Language Processing (NLP) and Computer Vision.
What You’ll Learn:
Fundamentals of Benchmarking:
Understanding AI benchmarking and its significance.
Differences between NLP and CV benchmarks.
Key metrics for effective evaluation.
Setting Up Your Environment:
Installing tools and frameworks like Hugging Face, Python, and CIFAR-10 datasets.
Building reusable benchmarking pipelines.
Working with Datasets:
Utilizing popular datasets like CIFAR-10 for Computer Vision.
Preprocessing and preparing data for NLP tasks.
Model Performance Evaluation:
Comparing performance of various AI models.
Fine-tuning and evaluating results across benchmarks.
Interpreting scores for actionable insights.
Tooling for Benchmarking:
Leveraging Hugging Face and OpenAI GPT tools.
Python-based approaches to automate benchmarking tasks.
Utilizing real-world platforms to track performance.
Advanced Benchmarking Techniques:
Multi-modal benchmarks for NLP and CV tasks.
Hands-on tutorials for improving model generalization and accuracy.
Optimization and Deployment:
Translating benchmarking results into practical AI solutions.
Ensuring robustness, scalability, and fairness in AI models.
Benchmark RAG implementations
RAGAS
Coherence
Confident AI - Deepeval
Hands-On Modules:
Implementing end-to-end benchmarking pipelines.
Exploring CIFAR-10 for image recognition tasks.
Comparing supervised, unsupervised, and fine-tuned model performance.
Leveraging industry tools for state-of-the-art benchmarking