350 skills found · Page 12 of 12
Emqo / AgentCliBridgeBridges CLI-based AI agents (Claude, Codex, Gemini) to chat platforms (Telegram, Discord) with scheduled a uto-tasks, autonomous project management, HITL approval, conditional branching, webhook triggers, parallel execution, and ob servability
xRiveria / CycloneA C++ multithreading library allowing for concurrent task executions, priority scheduling and loop parallelizations.
carrotly-ai / Gluon AgentAI coding agent orchestrator — run parallel Claude Code tasks in sandboxed Docker containers, monitor from a Kanban board or chat bot.
LUWENL / PDA GAParallel Dual Adaptive Genetic Algorithm based multi-satellite autonomous task allocation for moving target tracking
Yukarizz / CRTFSThe official implementation of "A color information driven dual task parallel network for RGB-T image fusion and saliency object detection." IJCV 2026
MirzaZuhaibBeg / OperationCustomOperation class will make API call and update the data model accordingly. This class can be used to add operation in operation queue to perform any sequenatial or parallel data tasks. Eg: adding multiple object to server which can not be added in single API and it needs to be added one by one. This class will take data model as input and it will return same data model by updating its state such as Success or Failure
amit21AIT / Artifitial Neural Network Churn ModelingBusiness Problem: Dataset of a bank with 10,000 customers measured lots of attributes of the customer and is seeing unusual churn rates at a high rate. Want to understand what the problem is, address the problem, and give them insights. 10,000 is a sample, millions of customer across Europe. Took a sample of 10,000 measured six months ago lots of factors (name, credit score, grography, age, tenure, balance, numOfProducts, credit card, active member, estimated salary, exited, etc.). For these 10,000 randomly selected customers and track which stayed or left. Goal: create a geographic segmentation model to tell which of the customers are at highest risk of leaving. Valuable to any customer-oriented organisations. Geographic Segmentation Modeling can be applied to millions of scenarios, very valuable. (doesn't have to be for banks, churn rate, etc.). Same scenario works for (e.g. should this person get a loan or not? Should this be approved for credit => binary outcome, model, more likely to be reliable). Fradulant transactions (which is more likely to be fradulant) Binary outcome with lots of independent variables you can build a proper robust model to tell you which factors influence the outcome. alt text Problem: Classification problem with lots of independent variables (credit score, balance, number of products) and based on these variables we're predicting which of these customers will leave the bank. Artificial Neural Networks can do a terrific job with Classification problems and making those kind of predictions. Libraries used: Theano numerical computation library, very efficient for fast numerical computations based on Numpy syntax GPU is much more powerful than CPU, as there are many more cores and run more floating points calculations per second GPU is much more specialized for highly intensive computing tasks and parallel computations, exactly for the case for neural networks When we're forward propogating the activations of the different neurons in the neural network thanks to the activation function well that involves parallel computations When errors are backpropagated to the neural networks that again involves parallel computation GPU is a much better choice for deep neural network than CPU - simple neural networks, CPU is sufficient Created by Machine Learning group at the Univeristy of Montreal Tensorflow Another numerical computation library that runs very fast computations that can run on your CPU or GPU Google Brain, Apache 2.0 license Theano & Tensorflow are used primarily for research and development in the deep learning field Deep Learning neural network from scratch, use the above Great for inventing new deep learning neural networks, deep learning models, lots of line of code Keras Wrapper for Theano + Tensorflow Amazing library to build deep neural networks in a few lines of code Very powerful deep neural networks in few lines of code based on Theano and Tensorflow Sci-kit Learn (Machine Learning models), Keras (Deep Learning models) Installing Theano, Tensorflow in three steps with Anaconda installed: $ pip install theano $ pip install tensorflow $ pip install keras $ conda update --all
1iveowl / ParallelTaskQueueRxParallel Task Queue with Reactive Extensions
eruffaldi / TaskscheduleScheduler for DAG of data-parallel tasks for Multicore systems
simone-sanfratello / SprinterRun parallel queued tasks
bainos / JobberParallel Task Runner
ctmakro / LlllPerform your python tasks in parallel.
welefen / Parallel Limitparallels task limited based on Promise
BioDepot / TaskPerform AWSLambdaComputational tasks performance in parallel using AWS Lambda and example
k32 / AnvlA general-purpose parallel task execution tool.
Aschen / Bash PromisesBash promise is a micro bash library composed of 6 functions allowing to execute tasks in parallel in a shell script using promises.
lyuheng / T FSM[SIGMOD'23] A Task-Based System for Massively Parallel Frequent Subgraph Pattern Mining from a Big Graph
ngConsulti / Gulp AdhocEasy sequential & parallel task composition on the command line.
linxabc / ItaskGolang task manager, including call chain, sync, async and parallel executor.
jtirana98 / Uth ThesisSupport for Parallel Drone-based Task Execution at Multiple Edge Points
