222 skills found · Page 4 of 8
huiali / Rust SkillsAn AI expert capability layer for Rust engineering practices, centered on modular skill orchestration and collaborative execution chains. It turns Rust’s core knowledge structures into callable reasoning and decision units, enabling diagnosis, design, and optimization in complex real-world scenarios.
PhasesResearchLab / PySIPFENNPython python toolset for Structure-Informed Property and Feature Engineering with Neural Networks. It offers unique advantages through (1) effortless extensibility, (2) optimizations for ordered, dilute, and random atomic configurations, and (3) automated model tuning.
KOSASIH / Ecobio Remediatech CoreThe ecobio-redemiatech-core repository serves as the foundational codebase for the EcoBio Redemiatech initiative. It includes essential algorithms, genetic engineering protocols, and synthetic biology tools designed for the development and optimization of genetically engineered microorganisms aimed at environmental remediation.
Swart47 / Lyra Prompt OptimizerAI agent to transform vague prompts into optimized requests using a 4-D prompt engineering framework.
keowu / SwiftstringinspectorA simple plugin for working with Swift Strings, optimized Swift Strings, and Swift Arrays during the reverse engineering of iOS binaries in Hex-Rays IDA.
Abhi0323 / Machine Learning Based Loan Default Early Warning SystemDeveloped an end-to-end ML system on Azure to predict loan defaults, leveraging advanced data preprocessing, feature engineering, and machine learning models to optimize accuracy. This project includes a comprehensive suite of tools and techniques for robust financial risk assessment, deployed to enhance decision-making for high-risk exposures.
abhishekdbihani / Home Credit Default Risk RecognitionThe project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
amerob / Ultimate Prompt Engineering PlaybookA collection of 114 notebooks covering modern prompt engineering techniques. Hands-on, runnable examples with real-world use cases, benchmarks, and failure analysis. Supports multiple LLMs, practical experimentation and systematic prompt optimization.
rich-iannone / Talk BoxLLM chatbots with attention-optimized prompt engineering
vincehass / Deep Reinforcement Learning Optimal ControlThis repository contains PyTorch implementations of deep reinforcement learning algorithms and environments for Robotics and Controls. The goal of this project is to include engineering applications for industrial optimization. I reproduce the results of several model-free and modelbased RL algorithms in continuous and discrete action domains.
Rick10119 / Data Driven Dimension ReductionData and code for our IEEE Power Engineering Letters paper "Data-Driven Dimension Reduction for Industrial Load Modeling Using Inverse Optimization".
THUHoloLab / Neural Pupil Engineering FPMThis is the MATLAB code for the implementation of neural pupil engineering FPM (NePE-FPM), an optimization framework for FPM reconstruction for off-axis areas.
oxbshw / Prompt Engineering GuidePrompt Engineering Guide provides practical techniques and strategies for crafting effective prompts to optimize AI model outputs. It’s designed to help users unlock more accurate, creative, and reliable responses from language models across a wide range of tasks.
kimimgo / Awesome AI CaeA curated list of 113 AI-ready tools for Computer-Aided Engineering — CFD, FEA, SPH, DEM, differentiable simulation, neural operators, PINNs, MCP servers. Python APIs, CLI, mesh generation, optimization.
rohanmistry231 / Prompt Engineering Interview PreparationA focused resource for mastering prompt engineering, featuring practice problems, examples, and interview-oriented techniques for optimizing AI model interactions. Covers crafting effective prompts for NLP models like GPT and BERT, with Python-based exercises.
gjkennedy / Ae6310Jupyter Notebooks for AE6310: Optimization for the Design of Engineering Systems
thieu1995 / EnoppyENOPPY: A Python Library for Engineering Optimization Problems
Soumyabrata111 / Optimization TechniquesThere are many optimization algorithms described in the book "Optimization of Engineering Design: Algorithms and Examples" by Prof. Kalyanmoy Deb. I will try to write each of those algorithms in programming languages like MATLAB, Python etc. Please let me if you find any bug.
OptiMaL-PSE-Lab / REINFORCE PSEReinforcement learning for batch bioprocess optimization (Computers & Chemical Engineering, 2020)
