65 skills found · Page 1 of 3
JuliaStats / Distances.jlA Julia package for evaluating distances (metrics) between vectors.
taki0112 / GAN Metrics TensorflowSimple Tensorflow implementation of metrics for GAN evaluation (Inception score, Frechet-Inception distance, Kernel-Inception distance)
AIPHES / Emnlp19 MoverscoreMoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Amarcolina / NativeSDFEvaluate signed-distance-fields with speed using Unity Jobs and Burst
YoonjinXD / KadtkA standardized toolkit of Kernel Audio Distance (KAD)—a distribution-free, unbiased, and computationally efficient metric for evaluating generative audio.
leoshuncheng / Vldbj Trajectory Distance MeasuresThe Java implementation of "A Survey of Trajectory Distance Measures and Performance Evaluation". VLDBJ 2020
DSL-Lab / FVMD Frechet Video Motion DistanceFréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in Videos
joisino / Reeval WmdCode for "Re-evaluating Word Mover’s Distance" (ICML 2022)
drscotthawley / Fad PytorchFrechet Audio Distance evaluation in PyTorch
Nelvinebi / Geospatial Suitability Analysis For Renewable Energy SitesThis project simulates geospatial analysis to assess land suitability for renewable energy sites using synthetic data. It evaluates solar irradiance, wind speed, slope, distance to roads, and land use to compute a suitability score and classify locations as low, moderate, or highly suitable for energy development.
JuliaStats / Distance.jlJulia module for Distance evaluation
abhilash1910 / BERTSimilaritySentential Semantic Similarity measurement library using BERT Embeddings for spatial distance evaluation.
suning-opensource / Frustrated Random WalkA graph algorithm for evaluating node distances.
WMD-group / XtalmetPython package containing a variety of distance functions for crystals, as well as evaluation metrics for crystal generation.
RishabhArya / Campus Navigation SystemDijkstra Algorithm is one of the most famous algorithms in computer science. There might be several possible routes to reach a destination point. If someone doesn’t travel through optimal path, it will consume more time and energy. This project aims to determine locations of the node that reflect all the nodes in the list, build the route by connecting nodes and evaluate the optimal path by using Dijkstra algorithm. Dijkstra’s Algorithm is also known as a single source shortest path algorithm which is used to find the shortest distance/path from one node to another node in a graph. This algorithm can be used only for positive distances from one location to another.
vkgnandhu177 / Bayesian Regression And Bitcoin# Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions).
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!
nicodjimenez / Fit NeuronA neuroscience python package for the estimation and evaluation of neural models from patch clamp neural recordings, including a library of spike distance metrics..
gkh178 / An Improved NLM Image Denoising Algorithm Based On Edge DetectionAiming at the removal of gaussian noise, we systematically analyze the shortage of non-local means image denonising algorithm (NLM), finding it is easy to lose structure information when dealing with the image containing complex edges and textures by NLM algorithm. In order to solve this problem, a non-local means image denoising based on edge detection is proposed in this thesis. The innovation of the proposed algorithm is mainly manifested in the following : (1) An improved Sobel operator with eight directions is proposed to extract a more accurate edge image; (2) To make the neighborhoods with similar structure obtain more weight, not only the Euclidean distance but also the edge image are considered when the similarity of neighborhoods is measured. Many experiments demonstrate that in both subjective and objective evaluation principles the performance of the improved algorithm has a good effect, and the visual effect of the denoised image is good.
ba-lab / DistevalDISTEVAL: A web-server for evaluating protein inter-residue distances
