Tutorial sull’IA

Discover how to get ChatGPT to give you the outputs you want by giving it the inputs it needs.

Learn how to use Stable Diffusion, an advanced open-source deep learning model that generates high-quality images from text descriptions. This tutorial covers the basics of how the model works and step-by-step instructions for running Stable Diffusion online and locally.

Unleash the full potential of GPT-3 through fine-tuning. Learn how to use the OpenAI API and Python to improve this advanced neural network model for your specific use case.

Dive into Explainable AI (XAI) and learn how to build trust in AI systems with LIME and SHAP for model interpretability. Understand the importance of transparency and fairness in AI-driven decisions.

Explore how AI agents Auto-GPT, AgentGPT & BabyAGI are revolutionizing autonomous AI systems. Learn about their applications, limitations & more.

Discover how to use ChatGPT for marketing, including some example prompts to get you started.

In this tutorial, you'll learn how to work with the OpenAI Python package to programmatically have conversations with ChatGPT.

LangChain is a framework for including AI from large language models inside data pipelines and applications. This tutorial provides an overview of what you can do with LangChain, including the problems that LangChain solves and examples of data use cases.

Discover the powerful capabilities of OpenAI Whisper Python API for transcription and translation. It comes with multi-language support and prompt enhancement for accurate transcription.

Learn how to use ChatGPT in a real-life end-to-end data science project. We will use it for project planning, data analysis, data preprocessing, model selection, hyperparameter tuning, developing a web app, and deploying it on the Spaces.

Discover what text classification is, how it works, and successful use cases. Explore end-to-end examples of how to build a text preprocessing pipeline followed by a text classification model in Python.

Learn how gradient descent optimizes models for machine learning. Discover its applications in linear regression, logistic regression, neural networks, and the key types including batch, stochastic, and mini-batch gradient descent.