17 skills found
hiroyuki-kasai / OLSTECOnLine Low-rank Subspace tracking by TEnsor CP Decomposition in Matlab: Version 1.0.1
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!
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.
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.
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.
liyipeng00 / ConvergenceConvergence Analysis of Sequential Federated Learning on Heterogeneous Data
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.
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.
kklmn / ParSeqPython software library for Parallel execution of Sequential data analysis.
fwt-team / VMF HMMSequentially spherical data modeling with hidden Markov models and its application to fMRI data analysis powered by@numpy
NeuraSearch / Spotify XRL Skipping PredictionAn investigation on the utility of users’ historical data for the task of sequentially predicting users’ music skipping behaviour using Deep Reinforcement Learning. The analysis is performed on the Spotify's Music Streaming Sessions Dataset. Published at CHIIR2023.
mzarnecki / Time Series AnalysisThis repository contains a collection of notebooks presenting different approaches to sequential data analysis, feature extraction, and the development of predictive and classification models. These materials can be used as examples for lectures, exercises, or self-study.
nf-core / SammyseqPipeline for Sequential Analysis of MacroMolecules accessibilitY sequencing (SAMMY-seq) data, to analyze chromatin state.
nschmandt / Financial CodeThe projects in the section are centered around the analysis of TD_sequential, and methods of analyzing this as well as data scrappers are present in this directory.
lordflavio / PEMF Time SeriesPredictive Estimation of Model Fidelity (PEMF) is a model-independent approach to measure the fidelity of surrogate models or metamodels, such as Kriging, Radial Basis Functions (RBF), Support Vector Regression (SVR), and Neural Networks. It can be perceived as a novel sequential and predictive implementation of K-fold cross-validation. PEMF takes as input a model trainer (e.g., RBF-multiquadric or Kriging-Linear), sample data on which to train the model, and hyper-parameter values (e.g., shape factor in RBF) to apply to the model. As output, it provides a predicted estimate of the median and/or the maximum error in the surrogate model. PEMF has been reported to be more accurate and robust than typical leave-one-out cross-validation, in providing surrogate model error measures (for various benchmark functions). The current version of PEMF has been implemented with RBF (included in this package), Kriging (DACE package), and SVR (Libsvm package), PEMF (has been and) can be readily used for the following purposes: 1. Surrogate model validation 2. Surrogate model uncertainty analysis 3. Surrogate model selection 4. Surrogate-based optimization (to guide sequential sampling) Other perceived broader applications of PEMF include testing of machine learning models and uncertainty analysis with data-driven models (and other areas where leave-one-out or k-fold cross-validation is typically used).
