49 skills found · Page 1 of 2
ROBAST / ROBASTROOT-based simulator for ray tracing (ROBAST) is a non-sequential ray-tracing simulation library developed for wide use in optical simulations of gamma-ray and cosmic-ray telescopes. The library is written in C++ and fully utilizes the geometry library of the ROOT analysis framework.
Antondfger / Does It Look SequentialCode for ACM RecSys 2024 paper 'Does It Look Sequential? An Analysis of Datasets for Evaluation of Sequential Recommendations' and the extended version.
XD3an / Python Sequential Thinking MCPA Python implementation of the Sequential Thinking MCP server using the official Model Context Protocol (MCP) Python SDK. This server facilitates a detailed, step-by-step thinking process for problem-solving and analysis.
pnfernandes / Python Code For Stress Constrained Topology Optimization In ABAQUSThis repository contains a Python code with five implementations of topology optimization approaches suitable for 2D and 3D problems, all considering bi-directional evolutionary structural optimization. The approaches implemented include both discrete and continuous methods, namely: - Optimality Criteria, for continuous or discrete variables; - Method of Moving Asymptotes; - Sequential Least Squares Programming (from SciPy module); - Trust-region (from SciPy module). The implementation of the Optimality Criteria method is suitable for compliance minimization problems with one mass or volume constraint. The implementation of the remaining methods is suitable for stress constrained compliance minimization and stress minimization problems, both with one mass or volume constraint. The code uses the commercial software ABAQUS to execute Finite Element Analysis (FEA) and automatically access most of the necessary information for the optimization process, such as initial design, material properties, and loading conditions from a model database file (.cae) while providing a simple graphic user interface. Although the code has been developed mainly for educational purposes, its modularity allows for easy editing and extension to other topology optimization problems, making it interesting for more experienced researchers. This code has been used in the article "Python code for 2D and 3D stress constrained topology optimization in ABAQUS: theory, implementation, and case studies" [1]. The folders included in this dataset contain the results obtained, as well as the information necessary to replicate them. In particular, the folder 'Validation' contains the data used to validate the functioning of the code provided. Notes: - Stress-dependent problems are only compatible with the following ABAQUS element types: CPE4, CPS4, 3DQ8, and S4. - The authorship of the functions 'mmasub' and 'subsolv' used in the Method of Moving Asymptotes are credited to Arjen Deetman. Source: https://github.com/arjendeetman/GCMMA-MMA-Python - Despite the validations performed, this program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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!
giobbu / CUSUMDifferent flavours of CUSUM for change point detection.
ruthkeogh / Sequential TrialsR code for implementation of the simulation study described in the paper: "Causal inference in survival analysis using longitudinal observational data: Sequential trials and marginal structural models"
M-Taghizadeh / NLP From RNN To TransformerIn this repository, based on the latest NLP pappers, we researched on sequential data and time series and developed tasks in NLP such as stock price prediction, time series prediction, sentiment analysis from text and We developed the language model and so on. This research is based on recurrent neural networks, LSTM networks and the new Transformer architecture and attention mechanism.
patogallardo / Zemax ToolsMisc Zemax tools for sequential optical analysis
hoangsonww / Earthquake R Analysis🌏 A project for visualizing and analyzing global earthquakes (M ≥ 2.5, last 30 days) using a single R script that automates data download, cleaning, and plotting. Generates 15 sequential plots covering spatial, temporal, and statistical patterns, including regression analysis of magnitude vs. depth.
llnl / SAC2000SAC2000 (Seismic Analysis Code for the third millennium) is a general purpose interactive program designed for the study of sequential signals, especially time-series data.
DivanshiJain2005 / Driver Drowsiness DetectionA real-time driver drowsiness detection system using Haar Cascade for face detection, LSTM for sequential analysis, and CNN for feature extraction, achieving 95.1% accuracy. The system monitors eye closure patterns and triggers alerts to prevent accidents and enhance road safety.
sehugg / StarthinkerThis is a C library for analysis and automatic play of sequential turn-based games such as chess, go, poker, etc.
y-z-zhang / Net Sync SymFinding the finest simultaneous block diagonalization of multiple matrices by sequentially exploring invariant subspaces under matrix multiplications. Applications include the efficient analysis of arbitrary synchronization patterns in complex networks.
liyipeng00 / ConvergenceConvergence Analysis of Sequential Federated Learning on Heterogeneous Data
KaiChengDM / Enhanced SDISEnhanced sequential directional importance sampling for structural reliability analysis
alexpiti / Paraxial OpticsA paraxial/Gaussian optics MATLAB toolkit for sequential 2D meridional plane ray-tracing. Intended for educational analysis of optical systems with arbitrary lenses and stops.
fazildgr8 / AppliedDeepLearningThis repository consists a set of Jupyter Notebooks with a different Deep Learning methods applied. Each notebook gives walkthrough from scratch to the end results visualization hierarchically. The Deep Learning methods include Multiperceptron layers, CNN, GAN, Autoencoders, Sequential and Non-Sequential deep learning models. The fields applied includes Image Classification, Time Series Prediction, Recommendation Systems , Anomaly Detection and Data Analysis.
rezamovahed93 / A Major Depressive Disorder Classifcation Framework Based On EEG Signals Using Statistical SpectralThis paper proposes a machine learning framework for MDD diagnosis, which uses different types of EEG-derived features. The features are extracted using statistical, spectral, wavelet, functional connectivity, and nonlinear analysis methods. The sequential backward feature selection (SBFS) algorithm is also employed to perform feature selection. Various classifier models are utilized to select the best one for the proposed framework.
yannTrm / Resnet 1DThis GitHub repository contains a specialized implementation of 1D Residual Networks (ResNets) for sequence data classification tasks. With tailored architectures and various ResNet variants, it offers efficient learning from 1D sequential data, making it ideal for applications such as time series analysis and sensor data classification.
