veneta13's Stars
tayllan/awesome-algorithms
A curated list of awesome places to learn and/or practice algorithms.
microsoft/graphrag
A modular graph-based Retrieval-Augmented Generation (RAG) system
KindXiaoming/pykan
Kolmogorov Arnold Networks
dair-ai/ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
pymc-devs/pymc
Bayesian Modeling and Probabilistic Programming in Python
greyhatguy007/Machine-Learning-Specialization-Coursera
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
opendilab/awesome-RLHF
A curated list of reinforcement learning with human feedback resources (continually updated)
rguo12/awesome-causality-algorithms
An index of algorithms for learning causality with data
matheusfacure/python-causality-handbook
Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.
jalammar/ecco
Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
explosion/sense2vec
🦆 Contextually-keyed word vectors
mistralai/cookbook
bashtage/linearmodels
Additional linear models including instrumental variable and panel data models that are missing from statsmodels.
matteocourthoud/awesome-causal-inference
A curated list of causal inference libraries, resources, and applications.
dformoso/sklearn-classification
Data Science Notebook on a Classification Task, using sklearn and Tensorflow.
jvpoulos/causal-ml
Must-read papers and resources related to causal inference and machine (deep) learning
slgero/testovoe
Home assignments for data science positions
bitlaw-jp/the-constitution-of-japan
mdipietro09/DataScience_ArtificialIntelligence_Utils
Examples of Data Science projects and Artificial Intelligence use-cases
rguo12/awesome-causality-data
A data index for learning causality.
shafaypro/CrackingMachineLearningInterview
A repository to prepare you for your machine learning interview, involving most of the questions asked by all the tech giants and local companies. Do this to Ace your Machine Learning Engineer Interviews
stelf/en2bg4term
речник с грижливо подбирани преводи на често срещани понятия от света на ИТ. приемат се предложения. прочетете по-долу как можете дас е включите.
piyushpathak03/Recommendation-systems
Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
patil-suraj/exploring-T5
A repo to explore different NLP tasks which can be solved using T5
SuyashLakhotia/UniversityNotes
:books: Repo to keep track of my undergraduate notes.
lukesalamone/lukesalamone.github.io
scala-fmi/scala-fmi-2024
triffon/lcpt-2018-19
Програми и експерименти от упражненията по λ-смятане и теория на доказателствата, 2018/19 г.
triffon/bgradios
Списък с URL на български радиостанции
triffon/lcpt-2021-22
Програми и експерименти от занятията по λ-смятане и теория на доказателствата, 2021/22 г.