Fine-Tuning OpenAI: Quando, Come, ROI vs Prompt Engineering
«Fine-Tuning OpenAI: Quando, Come, ROI vs Prompt Engineering» è un corso online di ChatGPT e OpenAI di livello principiante, tenuto da InsightLearn Curated AI su InsightLearn. Il percorso comprende 10 lezioni organizzate in 3 sezioni, per una durata complessiva di 5h 0m. Fine-tuning gpt-4o-mini e gpt-4o: dataset prep, training, evaluation, costi. Quando vale rispetto a prompt engineering puro.
- Docente: InsightLearn Curated AI
- Categoria: ChatGPT e OpenAI
- Livello: principiante
- Durata: 5h 0m
- Accesso: gratuito in questa fase di validazione, in cambio di un feedback al termine
- Certificato: certificato di completamento verificabile online
Descrizione del corso
InsightLearn è in fase di sviluppo (work in progress): i contenuti di questa pagina sono pubblicati esclusivamente a fini di verifica e test.
Fine-tuning gpt-4o-mini e gpt-4o: dataset prep, training, evaluation, costi. Quando vale rispetto a prompt engineering puro.
Cosa imparerai
- Curated from public resources. Fine-tuning gpt-4o-mini e gpt-4o: dataset prep, training, evaluation, costi. Quando vale rispetto a prompt engineering puro.
Programma del corso
- Quando fare fine-tuning (decision tree) (10 min)
- Modelli fine-tunable: 4o-mini, 4o, 3.5 (10 min)
- Costi: training, hosting, inference (10 min)
- Formato JSONL chat completions (10 min)
- Quality > quantity: principi (10 min)
- Validation split e baseline (10 min)
- Avviare training job (10 min)
- Monitor metrics (loss, accuracy) (10 min)
- Eval custom benchmark (10 min)
- Deploy + A/B test in production (10 min)
Dalle lezioni
Estratto della trascrizione di «Quando fare fine-tuning (decision tree)»
Hyperparameters are the parameters of the model that are not learned during the training process, but are set by the user before the training. They are configuration settings that control the learning process and model behavior. For example, the maximum depth to allow the decision tree to grow, number of hidden layers and nodes in neural networks, or the number of nearest neighbors to consider in the K-nearest neighbors algorithm. The choice of hyperparameters can significantly impact model performance, as they help. balance the trade-off between overfitting and underfitting. Let's see an example. In the KNN algorithm, the class of an observation is predicted by looking at the classes of its K nearest neighbors and performing a majority vote. The…
Estratto della trascrizione di «Modelli fine-tunable: 4o-mini, 4o, 3.5»
Hi guys, welcome to Mechanical Boost. Today, in this video, we are going to discuss the top causes of engine knocking noise and how to fix them. Engine knocking is a term given to the tapping, pinging noise from the engine. Knocking occurs when fuel burns unevenly in the engine cylinders. Reasons why engine is knocking. Engine knocking is a term given to the tapping, pinging noise from the engine cylinders. 1. Incorrect fuel octane. Engine knocking is a term given to the tapping, pinging noise from the engine cylinders. Rating using a lower octane fuel than recommended for your engine can lead to engine knock. Engine knocking is a term given to the tapping, pinging noise from the engine…
Estratto della trascrizione di «Costi: training, hosting, inference»
As the cost of running those AI workloads got you down in the dumps, yeah, it can get pretty expensive and fast. Today we're going to tackle that problem head on and show you how Google Cloud's AI hypercomputer can keep your cost down without sacrificing performance. Keep watching because in this video we're going to show you how to efficiently serve LLMs on GPUs with GKE Autopilot. So hold on tight. The problem's pretty straightforward. We build this amazing AI app and the next thing you know... you're looking at a bill that's a bit higher than you expected. A traditional approach might be to buy more power than you need just to be safe. But wait a second…
Altri corsi di ChatGPT e OpenAI
- Function Calling OpenAI: dalle Funzioni ai Workflow Agentici
- o-series Reasoning: o1, o3, o3-mini per Task Complessi
- Realtime API OpenAI: Voce, Video, Multimodalità in Streaming
- Structured Outputs OpenAI: JSON Conforme allo Schema Senza Validation Hell
- OpenAI Vision: GPT-4o Multimodal per Immagini, Documenti e Video Frame
- Whisper di OpenAI: Trascrizione Audio Production-Ready
Domande frequenti
Quanto dura il corso Fine-Tuning OpenAI: Quando, Come, ROI vs Prompt Engineering?
Il corso dura 5h 0m in totale, con 10 lezioni suddivise in 3 sezioni, da seguire online al proprio ritmo.
Serve esperienza pregressa?
Il corso è di livello principiante e non indica requisiti specifici di ingresso.
Il corso rilascia un certificato?
Sì: al completamento del corso ricevi un certificato di completamento verificabile online.