moonisali,Moonis Ali,github

ml-zoomcamp

ML Zoomcamp fall 2021 homework and stuff

moda

models and evaluation framework for trending topics detection

nebullvm

Plug and play modules to optimize the performances of your AI systems 🚀

neural-machine-translation--nmt-

Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. NMT departs from phrase-based statistical approaches that use separately engineered subcomponents.Neural machine translation (NMT) is not a drastic step beyond what has been traditionally done in statistical machine translation (SMT). Its main departure is the use of vector representations ("embeddings", "continuous space representations") for words and internal states. The structure of the models is simpler than phrase-based models. There is no separate language model, translation model, and reordering model, but just a single sequence model that predicts one word at a time. However, this sequence prediction is conditioned on the entire source sentence and the entire already produced target sequence. NMT models use deep learning and representation learning. The word sequence modeling was at first typically done using a recurrent neural network (RNN). A bidirectional recurrent neural network, known as an encoder, is used by the neural network to encode a source sentence for a second RNN, known as a decoder, that is used to predict words in the target language.Recurrent neural networks face difficulties in encoding long inputs into a single vector. This can be compensated by an attention mechanism which allows the decoder to focus on different parts of the input while generating each word of the output. There are further Coverage Models addressing the issues in such attention mechanisms, such as ignoring of past alignment information leading to over-translation and under-translation. Convolutional Neural Networks (Convnets) are in principle somewhat better for long continuous sequences, but were initially not used due to several weaknesses. These were successfully compensated for in 2017 by using "attention mechanisms". A attention-based model, the transformer architecture remains the dominant architecture for several language pairs.

news-mood

Project developed during the SICSS-Aachen-Graz (July 2022) investigating the variation of sentiment in news articles tweeted by @BBCBreaking

next_word_prediction

Using transformers to predict next word and predict <mask> word

nnti-ws2021-nlp-project

Saarland University NNTI WS2021 NLP Final Project

notebooks

Notebooks from YouTube videos

nyc_tree_spatial-viz-tree-change-files

includes jupyter notebooks used for data analysis in the project and blog post

octis

OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

omdenascraper-patient_info-

Omdena London Chapter's project : NLP to prevent self-harm for London teens

openai-cookbook

Examples and guides for using the OpenAI API

page_rank_twitter_graph

Discover Social Circles in Twitter Ego Network

phoenix

ML Observability in a Notebook - Uncover Insights, Surface Problems, Monitor, and Fine Tune your Generative LLM, CV and Tabular Models

piml-toolbox

PiML (Python Interpretable Machine Learning) toolbox for model development and model validation

polvol

Code for the Political Volatility paper (Chico and Scott)

practical-nlp-notebooks

This repository contains the material of the practical section of course: "Introduction to Natural Language Processing" as a part of BICS(Bachelor in Computer Science) program at university of Luxembourg. These notebooks where created by me and my colleage https://github.com/katyamatya/ .

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