Compilation of notebooks.

This repository contains HTML versions of various Jupyter notebooks. These files are accessible directly in a web browser, allowing for easy viewing and sharing of notebook content without requiring a Jupyter Notebook environment.

This repository provides Jupyter notebooks to interact with Mistral Large Language Models (LLMs) for tasks including chatbot development, retrieval-augmented generation, and text generation. These notebooks are designed to help users leverage Mistral models in a range of applications, from conversational AI to content generation.

This repository contains Jupyter notebooks for working with Anthropic Large Language Models (LLMs), providing tools to explore chat-based interactions, retrieval-augmented generation, and text generation. These notebooks serve as a practical introduction to leveraging Anthropic models for various applications.

This repository contains Jupyter notebooks to explore and utilize OpenAI's Large Language Models (LLMs) for various applications, including chatbots, retrieval-augmented generation, text generation, prompt engineering, and vector embedding. These notebooks provide a comprehensive toolkit for working with OpenAI models in diverse contexts.

🤖 Deep Generative AI Projects 2026 - Notebooks & Code Guide

🤖 Deep Generative AI Projects 2026 - Notebooks & Code Guide

the notebook and generated texts created for the DAGPap22

A jupyter notebook to generate song lyrics using LSTM network.

Generates ballads using Deep learning . Using LSTMs and data of some famous ballads . Generates new ballads and autocompletes with initial given texts .

This repository contains a machine learning model designed to generate grading rubrics based on a given question and a sample answer. The model has been fine-tuned on custom data and implemented in a Jupyter Notebook.

🔍 Explore deep generative models with this structured repository for experiments, utilities, and course assignments using notebooks and scripts.

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Sales Script AI is a tool that generates tailored sales scripts using AI-driven natural language processing techniques in an interactive Jupyter Notebook environment.

Hands‑on experiments with neural sequence models. To generate text and tackle translation. Each colab notebook go through data prep, model building, training loops and evaluation.

Natural language processing (NLP) tasks: text classification and text generation. The notebooks explore different techniques and models for handling these tasks, offering insights into common challenges and solutions.

NeoTutor is an interactive Agentic AI tutor system powered by LLaMA 3 and LangGraph. It dynamically generates questions, provides structured feedback, and offers personalized practice—all within a single notebook. Ideal for students and educators looking to explore adaptive learning using large language models.

In this notebook, I'll construct a character-level LSTM with PyTorch. The network will train character by character on some text, then generate new text character by character. As an example, I will train on Anna Karenina. This model will be able to generate new text based on the text from the book!

Explore advanced neural networks for crafting captivating headlines! Compare LSTM 🔄 and Transformer 🔀 models through interactive notebooks 📓 and easy-to-use wrapper classes 🛠️. Ideal for content creators and data enthusiasts aiming to automate and enhance headline generation ✨.

Text generation using a character-based RNN with LSTM cells. We will work with a dataset of Shakespeare's writing from Andrej Karpathy's The Unreasonable Effectiveness of Recurrent Neural Networks. Given a sequence of characters from this data ("Shakespear"), train a model to predict the next character in the sequence ("e"). Longer sequences of …