Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets
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Updated
Jun 18, 2024 - Jupyter Notebook
Colab Compatible FastAI notebooks for NLP and Computer Vision Datasets
Our goal is to train a classifier that can predict the CEFR level of any given sentence. In this notebook we will use 🤗Hugging Face and its transformers library as the training framework, with Pytorch as the deep learning backend.
A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.
This repository contains a compilation of my Python projects and notebooks focusing on Machine Learning and Deep Learning.
A handwritten digits classifier of the MNIST dataset using PyTorch neural networks and Jupyter Notebook.
Data tools in Jupyter notebooks served from a container. Includes examples of cleaning, classification, clustering, graph drawing, and principal component analysis.
Classifier that identifies Greek text as Cypriot Greek or Standard Modern Greek
A CNN image classification model
Interactive Jupyter notebooks running Brainome to measure your data and create ML classifiers.
This repository contains the Jupyter notebooks of SAKI machine learning classification sessions.
Implementation of a series of Neural Network architectures in TensorFow 2.0
A collection of classification algorithms for different purposes
This repository contains a fully trained neural network which is used as a binary classifier to distinguish clearwood and defects. Several Jupyter Notebooks are presented in the GitHub page to help you use the wood classifier.
Bird Classifier developped in tensorflow using pre-trained model from Tensorflow Hub and running on Google Colab
A click bait classifier notebook developed using LSTM. The notebook showcases the analysis on Click bait heading data and a neural network to classify Heading as click bait. The model accuracy is 96%+.
MLP classification model
I have created a classifier to classify website, whether it is benign or Malicious with using the CRISP DM concepts in it. Additionally I wrote blog on it https://medium.com/@prashantjadiya/process-of-classifying-malicious-and-benign-websites-815cc2b42435.
This is IPython notebook in which i have used neural network to classify the mnist fashion dataset into 10 different categories based on their features
A classification approach to the machine learning Titanic survival challenge on Kaggle.Data visualisation, data preprocessing and different algorithms are tested and explained in form of Jupyter Notebooks
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