#学习与技能提升#A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2025 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art t...
#新手入门#ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP
#自然语言处理#collections of data science, machine learning and data visualization projects with pandas, sklearn, matplotlib, tensorflow2, Keras, various ML algorithms like random forest classifier, boosting, etc
#自然语言处理#List of DL topics and resources essential for cracking interviews
distfit is a python library for probability density fitting.
#计算机科学#Second edition of Springer Book Python for Probability, Statistics, and Machine Learning
Random vectors: marginal and conditional distributions. Normal, t-distribution, Chi-square and F-distribution... AND A LOT MORE.
This repository includes academic notes, study materials, and resources from B.Tech (Hons) in CSE, specializing in Artificial Intelligence and Data Science. It features question papers, proprietary st...
#计算机科学#Collection of all courses, and their materials, attended at Politecnico di Milano during both Bachelor level degree and Master level degree in Engineering, Computer Science Engineering
#计算机科学#Machine learning resources (Jupyter notebooks mostly). Originally code to complement the "EECE 5644: Introduction to Machine Learning and Pattern Recognition" course taught at Northeastern University.
pg_math extension to support statistical distribution functions for PostgreSQL
A math resource for CS student
#计算机科学#A curated list of references to help you get up to speed with the concepts and techniques needed to become a successful ML researcher.
#算法刷题#All the homeworks, testers and projects done at Marmara University, Computer Science & Engineering
#算法刷题#Projects of a CSE student at Marmara University
Subset simulation is a method of estimating low probability events. Here I adapt SS to perform well with correlated inputs.
♣️ ♦️ ♥️ ♠️ Train yourself for live Texas Holdem games by seeing the changing probability of winning as more cards are dealt.
Interactive courseware module that addresses common foundational-level concepts taught in statistics courses.
Trimmed L-moments and L-comoments for robust statistics.