AI チュートリアル

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.

Learn about the most popular deep learning model RNN and get hands-on experience by building a MasterCard stock price predictor.

The tutorial answers the most frequently asked questions about deep learning and explores various aspects of deep learning with real-life examples.

We help simplify sentiment analysis using Python in this tutorial. You will learn how to build your own sentiment analysis classifier using Python and understand the basics of NLP (natural language processing).