The repository includes Jupyter notebooks with deep learning examples in Python.

Fine-Tune Your Own Llama 2 Model LOCALLY in a Colab Notebook

Dive into Diverse Topics with Hands-on Python Notebooks

Stable diffusion One-click fine tuning colab notebook (A100)

notebooks to finetune `bert-small-amharic`, `bert-mini-amharic`, and `xlm-roberta-base` models using an Amharic text classification dataset and the transformers library

The notebook shows how deep learning tools (TensorFlow/Keras and PyTorch ) work in practice.

This Notebook contains the code for Fine tuning OpenAI GPT-3.5-Turbo API to specialize on answering the algerians BAC students questions within the Algeria Bac Context !!!

Colab notebook for finetuning Microsoft's Phi-2-3B LLM for solving mathematical word problems using QLoRA

This repository contains the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.

Can be used as-is for better slashed zeros recognition (especially with the Consolas font). This Repository includes a Jupyter notebook with instructions to train/finetune a Tesseract OCR model.

Part of M.Sc. Computer Science project. Demo Jupyter notebook/Python scripts to download a pretrained language model from huggingface and finetune it according to own topic domain and needs.

Pre-Training and Fine-Tuning transformer models using PyTorch and the Hugging Face Transformers library. Whether you're delving into pre-training with custom datasets or fine-tuning for specific classification tasks, these notebooks offer explanations and code for implementation.

This repo gathers the different notebooks created in the context of Micro Club's 1st wave of internal trainings

This repository offers practical notebook examples for fine-tuning large language models (LLMs) at no cost, using Google Colab's free GPU resources. Consider starring it if helpful. Feel free to contribute or suggest improvements.

It is end-to-end CNN Image Classification Model notebook which identifies the food in your image. In this project I was working with pre-trained classification model EfficientNetB1 and Food101 Dataset.

In this repository you can find the jupyter notebooks used to take part at the competitions created for the Artifical Neural Networks and Deep Learning exam at Politecnico di Milano.

Colab notebook where I replicate the Vision Transformer research paper from scratch in PyTorch on 'AI generated vs Real images dataset' and also fine-tune a pre-trained ViT-B/16 model on the same dataset to compare its performance

A tutorial that guides users through the process of fine-tuning a stable diffusion model using HuggingFace's diffusers library. The tutorial includes advice on suitable hardware requirements, data preparation using the BLIP Flowers Dataset and a Python notebook, and detailed instructions for fine-tuning the model.

In this notebook, i have tried to appy KMeans, Hierarchical and DBSCAN clustering along PCA. The dataset used is Mall_Customers. In DBSCAN, certain type of Heatmaps are used to find the Epsilon and min_samples value which have performed quite well in identifying the correct number of clusters.