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algorithms-in-python-r

Here you can find some algorithms like Genetic Algorithm, Gradient Descent, Reinforcement Learning, non linear programming..

ark-tweet-nlp-python

Simple Python wrapper around runTagger.sh of ark-tweet-nlp

awesome-conformal-prediction

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

awesome-langchain

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

awesome-machine-learning

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

biogpt

calfram

Calibration Framework for Machine Learning and Deep Learning

calibration-framework

Python Framework to calibrate confidence estimates of classifiers like Neural Networks

calibration.github.io

camoscio

Camoscio: An Italian instruction-tuned LLaMA

cifar_pytorch-lightning

CIFAR10, CIFAR100 results with VGG16,Resnet50,WideResnet using pytorch-lightning

cloths_segmentation

Code for binary segmentation of cloths

colossalai

Making large AI models cheaper, faster and more accessible

data-management

Big Data, Data Cleaning, Data Integration (record linkage), MongoDb, Data Analysis (Python language)

data-vizualization

Some Data Viz with ggplot2 library in R

deep-learning

Project and assignments for deep learning task

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

deepmoji

State-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc.

deploying-machine-learning-models

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

face_mask_detector

Deep neural model for classification task

focal_calibration

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

fortuna

A Library for Uncertainty Quantification.

gabornet

gpt4all

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

high-dimensional-data-

instructgoose

Implementation of Reinforcement Learning from Human Feedback (RLHF)

irony-sarcasm-detection

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.

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

langchain-tutorials

Overview and tutorial of the LangChain Library

llm-gpt-demo

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