lorenzofamiglini

awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD theses, articles and open-source libraries.

BioGPT

CalFram

Calibration Framework for Machine Learning and Deep Learning

Face_Mask_Detector

Deep neural model for classification task

fortuna

A Library for Uncertainty Quantification.

Irony-Sarcasm-Detection-Task

The detection of irony and sarcasm is one of the most insidious challenges in the field of Natural Language Processing. Over the years, several techniques have been studied to analyze these rhetorical figures, trying to identify the elements that discriminate, in a significant way, what is sarcastic or ironic from what is not. Within this study, some models that are state of the art are analyzed. As far as Machine Learning is concerned, the most discriminating features such as part of speech, pragmatic particles and sentiment are studied. Subsequently, these models are optimized, comparing Bayesian optimization techniques and random search. Once, the best hyperparameters are identified, ensemble methods such as Bayesian Model Averaging (BMA) are exploited. In relation to Deep Learning, two main models are analyzed: DeepMoji, developed by MIT, and a model called Transformer Based, which exploits the generalization power of Roberta Transformer. As soon as these models are compared, the main goal is to identify a new system able to better capture the two rhetorical figures. To this end, two models composed of attention mechanisms are proposed, exploiting the principle of Transfer Learning, using Bert Tweet Model and DeepMoji Model as feature extractors. After identifying the various architectures, an ensemble method is applied on the set of approaches proposed, in order to identify the best combination of algorithms that can achieve satisfactory results. Frameworks used: Pytorch, TF 2.0, Scikit Learn, Scikit-Optimize, Transformers

Object-and-Sound-detection

Application of Deep Learning and Machine Learning techniques for traffic sing detection and word trigger recognition

Statistical-Modeling

How to deal with statistical analysis

Text-Mining---Toxic-comment

Several models were developed in order to solve the text classification task on toxic comments. Several models have been created trying to give an innovative approach to solve the unbalanced class problem.

The-Maze-Problem

Implementation of traditional,hybrid algorithms and Reinforcement Learning

lorenzofamiglini/Irony-Sarcasm-Detection-Task

The detection of irony and sarcasm is one of the most insidious challenges in the field of Natural Language Processing. Over the years, several techniques have been studied to analyze these rhetorical figures, trying to identify the elements that discriminate, in a significant way, what is sarcastic or ironic from what is not. Within this study, some models that are state of the art are analyzed. As far as Machine Learning is concerned, the most discriminating features such as part of speech, pragmatic particles and sentiment are studied. Subsequently, these models are optimized, comparing Bayesian optimization techniques and random search. Once, the best hyperparameters are identified, ensemble methods such as Bayesian Model Averaging (BMA) are exploited. In relation to Deep Learning, two main models are analyzed: DeepMoji, developed by MIT, and a model called Transformer Based, which exploits the generalization power of Roberta Transformer. As soon as these models are compared, the main goal is to identify a new system able to better capture the two rhetorical figures. To this end, two models composed of attention mechanisms are proposed, exploiting the principle of Transfer Learning, using Bert Tweet Model and DeepMoji Model as feature extractors. After identifying the various architectures, an ensemble method is applied on the set of approaches proposed, in order to identify the best combination of algorithms that can achieve satisfactory results. Frameworks used: Pytorch, TF 2.0, Scikit Learn, Scikit-Optimize, Transformers

lorenzofamiglini/CalFram

Calibration Framework for Machine Learning and Deep Learning

lorenzofamiglini/awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD theses, articles and open-source libraries.

lorenzofamiglini/fortuna

A Library for Uncertainty Quantification.

lorenzofamiglini/BioGPT

lorenzofamiglini/awesome-langchain

😎 Awesome list of tools and projects with the awesome LangChain framework

lorenzofamiglini/awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

lorenzofamiglini/calibration-framework

Python Framework to calibrate confidence estimates of classifiers like Neural Networks

lorenzofamiglini/calibration.github.io

lorenzofamiglini/camoscio

Camoscio: An Italian instruction-tuned LLaMA

lorenzofamiglini/ColossalAI

Making large AI models cheaper, faster and more accessible

lorenzofamiglini/deepchecks

Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort. See our docs: https://docs.deepchecks.com

lorenzofamiglini/deploying-machine-learning-models

Example Repo for the Udemy Course "Deployment of Machine Learning Models"

lorenzofamiglini/focal_calibration

Code for the paper "Calibrating Deep Neural Networks using Focal Loss"

lorenzofamiglini/gpt4all

gpt4all: a chatbot trained on a massive collection of clean assistant data including code, stories and dialogue

lorenzofamiglini/instructGOOSE

Implementation of Reinforcement Learning from Human Feedback (RLHF)

lorenzofamiglini/langchain-tutorials

Overview and tutorial of the LangChain Library

lorenzofamiglini/llm-gpt-demo

🐍

lorenzofamiglini/medplum

Medplum is a healthcare platform that helps you quickly develop high-quality compliant applications.

lorenzofamiglini/missingno

Missing data visualization module for Python.

lorenzofamiglini/notebooks

Notebooks using the Hugging Face libraries 🤗

lorenzofamiglini/optbinning

Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.

lorenzofamiglini/pytorch-cifar

95.47% on CIFAR10 with PyTorch

lorenzofamiglini/pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

lorenzofamiglini/roco-dataset

Radiology Objects in COntext (ROCO): A Multimodal Image Dataset

lorenzofamiglini/SciencePlots

Matplotlib styles for scientific plotting

lorenzofamiglini/sentence-transformers

Multilingual Sentence & Image Embeddings with BERT

lorenzofamiglini/vit-pytorch

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

lorenzofamiglini/whisper-gpt3-streamlit

Whisper in combination with GPT-3

lorenzofamiglini/whisper-playground

Build real time speech2text web apps using OpenAI's Whisper https://openai.com/blog/whisper/