Popular GitHub Repositories for Deal Sourcing

A practical architecture styles for migrating from legacy monolith to modern cloud native application with the latest technologies and architectures like Vertical Slice Architecture, Event Sourcing, CQRS, DDD, gRpc, MongoDB, RabbitMq, and Masstransit in .Net 9.

Code implementation of "Learning Efficient Online 3D Bin Packing on Packing Configuration Trees". We propose to enhance the practical applicability of online 3D Bin Packing Problem (BPP) via learning on a hierarchical packing configuration tree which makes the deep reinforcement learning (DRL) model easy to deal with practical constraints and well-performing even with continuous solution space.

Code used for sourcing and cleaning the BigScience ROOTS corpus

A distributed system that simulates a retail experience coupled with some futuristic shopping robots.

A distributed system that simulates a retail experience coupled with some futuristic shopping robots.

Event Driven Microservices with CQRS, Saga, Event Sourcing - Code examples

Generate a wiki for your research topic, sourcing from the web and your docs.

AI eXplainable Inference & Search. Open Sourcing on-premise, ultra-fast latency intelligence to all.

The better way to deal with MNIST model and Core ML in iOS

The PyTorch implementation for "DEAL: Disentangle and Localize Concept-level Explanations for VLMs" (ECCV 2024 Strong Double Blind)

The traditional machine learning models give a lot of pain when we do not have sufficient labeled data for the specific task or domain we care about to train a reliable model. Transfer learning allows us to deal with these scenarios by leveraging the already existing labeled data of some related task or domain. We try to store this knowledge gained in solving the source task in the source domain and apply it to our problem of interest. In this work, I have utilized Transfer Learning utilizing BertForSequenceClassification model. Also tried RobertaForSequenceClassification and XLNetForSequenceClassification models for Fine-Tuning the Model.

Machine Learning + LSTM Implementation to capture sentiments surrouding the Colombian elections in 2018. Sentiment analysis entirely on Spanish tweets with interesting external data sourcing.

A multi-source supplier risk dataset combining KG scores, semantic similarity, and external disruption signals ideal for supply chain resilience research. Useful for researchers working on supplier risk assessment, multi-objective optimization, or reinforcement learning in dynamic sourcing environments.

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. Deep Learning algorithms are unable to deal with textual data in their natural language data form which is typically unstructured information; they require special representation of data as inputs instead. Usually, natural language text data needs to be converted into internal representations form that DL algorithms can read such as feature vectors, hence the necessity to use representation learnin

List of ? packages that deal with mathematics

Code to do multi agent systems research on StarCraft II. It contains methods to deal with the Raw Interface from the SC2 API, which has not been officially adapted for Python. This means that, the programmer can now access the objects at the game and the 2D original representation of the map

This repository contains the source code for my Thesis in the Department of Electrical and Computer Engineering at Aristotle University of Thessaloniki. The scope of this project is the employment of Computer Vision and Natural Language Processing techniques to deal with the complex problem of Food Categorization. Specifically, this project evaluates mainly two different classification techniques: the classification using the visual features of an image and the classification with the intermediate step of predicting a list of ingredients.

The proposed model is able to predict the evaluation of both grammatical coherence, vocabulary and grammatical conventions, so that the evaluation can give each of those criteria a value between 1 and 5, I did not treat the system as a classification process, but rather it was treated as a REGRESSION issue. It includes several steps through which a few errors were reached, all ranging between 0.25 for each criterion. The values ??of the weights that were reached can also be used to deal with the issue as a classification process (but it was not dealt with as well in this proposed methodology).

DealScale is a cutting-edge platform that leverages AI to transform business operations, providing intelligent solutions for deal management, customer engagement, and data-driven decision making.

A complete, production-ready SaaS platform for managing sales operations with AI-powered lead scoring, deal forecasting, and pipeline automation.