Build real C projects with 18 beginner-friendly challenges. Learn by doing with guided coding exercises and practical applications.

Combinatorial optimization layers for machine learning pipelines

Mining Argument Structures with Expressive Inference (Linear and LSTM Engines)

Structured Gradient Tree Boosting

Sequence Segmentation using Joint RNN and Structured Prediction Models (ICASSP 2017)

Automatic Measurement of Vowel Duration for Consonant Vowel Consonant (CVC) sound files (JASA 2016)

Risk Minimization Algorithms in Structured Prediction (JMLR 2016)

Build real Golang projects with 5 beginner-friendly challenges. Learn by doing with guided coding exercises and practical applications.

An open source package for performing supervised learning in structured prediction tasks. It implements conditional random fields (CRFs) models from (1) first-order to higher-order linear-chain CRFs, and from (2) first-order to higher-order semi-Markov CRFs (semi-CRFs)

Application of InferOpt.jl to the Stochastic Vehicle Scheduling Problem

Code for Joint Modeling of NER, Relation Extraction and Coreference Resolution using Constrained Conditional Models

Free Golang tutorials for beginners with 10 interactive lessons. Easy-to-follow programming guides with hands-on practice exercises.

A curated collection of free, high-quality courses from top universities like MIT, Stanford, and Princeton, designed to provide an equivalent education to an undergraduate Computer Science degree using Python. Ideal for self-learners aiming to efficiently master computer science fundamentals without the need for general education courses.

CL Algorithm for Learning Bayesian Network Structures

A virtual machine setup providing common code and resources for the Relational Databases and Full Stack Fundamentals courses, designed to streamline the learning experience for full-stack development students.

Explore coding skills through hands-on projects using Python, C#, JavaScript, SQL, and tools like Docker for practical developer experience.

An algorithm to detect leaders in social groups, based on Structured Support Vector Machines.

Structured world model with physics-validated predictions. Architecture validated on continuous dynamics (v2.0). Built for robotics applications. ```