Search Results for "cost estimation"
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Showing 13 open source projects for "cost estimation"
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Tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models
tiktoken is a high-performance, tokenizer library (based on byte-pair encoding, BPE) designed for use with OpenAI’s models. It handles encoding and decoding text to token IDs efficiently, with minimal overhead. Because tokenization is a fundamental step in preparing text for models, tiktoken is optimized for speed, memory, and correctness in model contexts (e.g. matching OpenAI’s internal tokenization). The repo supports multiple encodings (e.g. “cl100k_base”) and lets users switch encoding... -
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PyMC3
Probabilistic programming in Python
... in a model is not a scalar value or a fixed-length vector, but a function. A Gaussian process (GP) can be used as a prior probability distribution whose support is over the space of continuous functions. PyMC3 provides rich support for defining and using GPs. Variational inference saves computational cost by turning a problem of integration into one of optimization. PyMC3's variational API supports a number of cutting edge algorithms, as well as minibatch for scaling to large datasets. -
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Function Point Analysis
Progressive Function Point Analysis Workbook in Excel
Progressive Function Point Analysis Book Released: Print Copy: http://www.amazon.com/Progressive-Function-Point-Analysis-Estimation/dp/1502354160 EBook: http://www.amazon.com/dp/B00NH0MMAG Please support this project and its future releases ! Managers provide the architect/teams with project requirements. The architect/team returns detailed task list with estimated man hours per task making life easier for Project Managers with less guesstimates and proper business/technical process... -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
... and comparison. The library provides automatic path finding and cost estimation, exposing when contractions will explode in memory and suggesting better orders. Because it supports backends such as NumPy, TensorFlow, PyTorch, and JAX, the same model can run on CPUs, GPUs, or TPUs with minimal code changes. Tutorials and visualization helpers make it easier to understand how network topology affects expressive power and computational cost. -
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libRSF
A robust sensor fusion library for online localization
The libRSF is an open source C++ library that provides several components that are required to estimate the state of a (robotic) system based on probabilistic methods. By applying the factor graph concept, well known from Graph SLAM, libRSF provides a robust solution for many sensor fusion problems. The general idea of factor graphs is to describe the state estimation problem as a graph of nodes (the state variables) that are connected by factors (measurements). The resulting graph optimization... -
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java-string-similarity
Implementation of various string similarity and distance algorithms
... below. The "cost" column gives an estimation of the computational cost to compute the similarity between two strings of length m and n respectively. If the alphabet is finite, it is possible to use the method of four russians (Arlazarov et al. "On economic construction of the transitive closure of a directed graph", 1970) to speedup computation. This was published by Masek in 1980 ("A Faster Algorithm Computing String Edit Distances"). -
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ConvNet Burden
Memory consumption and FLOP count estimates for convnets
convnet-burden is a MATLAB toolbox / script collection estimating computational cost (FLOPs) and memory consumption of various convolutional neural network architectures. It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs. Estimation of memory consumption (e.g. feature map sizes... -
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Cenobi
cost estimation and management accounting, using neural networks
Cenobi is designed for management accountants, not (only) for statisticians and data mining experts. Carefully arranged default settings make sure you can concentrate on Cenobi's many accounting features rather than worrying about setting up artificial neural networks or genetic algorithms, which are the main machine learning tools under Cenobi's hood. Cenobi's main benefits are: - ease of use - Utilizing artificial neural networks to estimate cost relationships, Cenobi is able... -
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FFME
Key points detection
This method is a SIFT-like one, but specifically designed for egomotion computation. The key idea is that it avoids some of the steps SIFT gives, so that it runs faster, at the cost of not being so robust against scaling. The good news is that in egomotion estimation the scaling is not so critical as in registration applications, where SIFT should be selected. -
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PairedComparison
The Software Estimation Tool
The blog contains the milestones the project has reached, new features incorporated and step-by-step user guides. The Github Repository is here - https://github.com/sidharthamani/PairedComparison -
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The set of available multi-objective optimization algorithms continues to grow. This fact can be partially attributed to their widespread use and applicability. However this increase also suggests that there are several issues that remain to be addressed satisfactorily. One such issue is the diversity and the number of solutions available to the decision maker. Even for algorithms very well suited for a particular problem, it is difficult - mainly due to the computational cost - to use...
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Software Estimation Library
Software Estimation Library
Estimate effort, time and cost to develop a software project is one of the most challenging task for IT professionals. There are several reasons for this, but the absence of technique can be a major one. To address this issue the Estimation Library has intended to be used as a tool to perform software estimation calculations in conformity with commonly estimation techiniques. -
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An application that estimates machine cycle times for the injection molding of plastic parts in a variety of resins. Useful for rough cost estimation.
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