Search Results for "number prediction algorithm"
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Showing 32 open source projects for "number prediction algorithm"
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1
SAHI
A lightweight vision library for performing large object detection
... challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. -
2
WAFW00F
WAFW00F allows one to identify and fingerprint Web App Firewall
The Web Application Firewall Fingerprinting Tool. Sends a normal HTTP request and analyses the response; this identifies a number of WAF solutions. If that is not successful, it sends a number of (potentially malicious) HTTP requests and uses simple logic to deduce which WAF it is. If that is also not successful, it analyses the responses previously returned and uses another simple algorithm to guess if a WAF or security solution is actively responding to our attacks. For further details, check... -
3
NannyML
Detecting silent model failure. NannyML estimates performance
... science, empowering data scientist to quickly understand and automatically detect silent model failure. By using NannyML, data scientists can finally maintain complete visibility and trust in their deployed machine learning models. When the actual outcome of your deployed prediction models is delayed, or even when post-deployment target labels are completely absent, you can use NannyML's CBPE-algorithm to estimate model performance. -
4
Bayesian Optimization
Python implementation of global optimization with gaussian processes
This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic... -
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5
TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences... -
6
Python Outlier Detection
A Python toolbox for scalable outlier detection
... network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
7
SHAP
A game theoretic approach to explain the output of ml models
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark... -
8
Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior... -
9
LightFM
A Python implementation of LightFM, a hybrid recommendation algorithm
LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of BPR and WARP ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high-quality results. It also makes it possible to incorporate both item and user metadata into the traditional matrix factorization algorithms. It represents each user and item as the sum of the latent representations... -
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10
Scrapyd
A service daemon to run Scrapy spiders
Scrapyd can manage multiple projects and each project can have multiple versions uploaded, but only the latest one will be used for launching new spiders. A common (and useful) convention to use for the version name is the revision number of the version control tool you’re using to track your Scrapy project code. For example: r23. The versions are not compared alphabetically but using a smarter algorithm (the same packaging uses) so r10 compares greater to r9, for example. Scrapyd... -
11
Advanced Trigonometry Calculator
Precision Trigonometry: Advanced Calculator for Complex Math
Advanced Trigonometry Calculator is equipped with a user-friendly interface that allows for easy input of problems and instant computation. Professionals such as engineers who need to perform advanced trigonometric calculations in their work will find this tool extremely useful. More info by clicking below: https://advantrigoncalc.sourceforge.io/ Advanced Trigonometry Calculator was only and always only developed by the Portuguese Renato Alexandre dos Santos Freitas. Also author of... -
12
Sudoku Maker is a generator for Sudoku number puzzles. It uses a genetic algorithm internally, so it can serve as an introduction to genetic algorithms. The generated Sudokus are usually very hard to solve -- good for getting rid of a Sudoku addiction.
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13
U-Net Fusion RFI
U-Net for RFI Detection based on @jakeret's implementation
See original code here: https://github.com/jakeret/tf_unet Currently this project is based on Tensorflow 1.13 code base and there are no plans to transfer to TF version 2. The primary improvements to this code base include a training and evaluation framework, along with a fusion based approach to detection, combining a number of models (currently hard coded to two trained models) along with Sum Threshold as an additional "expert." Additional work is being done to add custom layers... -
14
DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
15
CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
16
Coach
Enables easy experimentation with state of the art algorithms
... to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning. -
17
CTS Surveyor
Foot traffic and facial analytics for your business and home
Surveyor is a software solution that monitors its environment via camera and gathers demographic information about the public in the surrounding area, providing important statistics such as number of people passing by as well as providing facial analytics to classify the pedestrians based on their age and gender. The statistical data is stored in a local database and is made available via RESTful API’s, and easy integration with other applications can be accomplished via a WebSocket interface... -
18
DACO-algorithm
A novel transcription factor complex prediction algorithm.
Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. Complexation of transcription factors and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enable to infer controlled genes as well as the exerted regulatory mechanism. We developed DACO (domain-aware cohesiveness optimization), a novel algorithm that combines protein-protein... -
19
D-Tailor
D-Tailor: automated analysis and design of DNA sequences
Recent advances in DNA cloning and synthesis technologies afford high throughput implementation of designed sequences into living cells. However, our ability to design sequences to interrogate multifactorial biological processes and further engineer biological functions is lagging behind. DNA-Tailor (D-Tailor) is a fully extendable software framework for biological sequence analysis and multi-objective sequence design. D-Tailor permits the seamless integration of an arbitrary number... -
20
AI learning
AiLearning, data analysis plus machine learning practice
..., and online earning community, with a QQ group of more than 10,000 people and at least 10,000 subscribers. The number of Github Stars exceeds 60k, and it ranks in the top 100 of all Github organizations. The daily up of all its websites exceeds 4k, and the peak of Alexa ranking is 20k. Our core members are certified as CSDN blog experts and short-book programmers as excellent authors. We have established ApacheCN, a non-profit document, and tutorial translation project. -
21
pySimiam
Robot simulator inspired by Sim.I.Am
PySimiam is a robot simulator, created to follow the course "Control of Mobile Robots" offered at Coursera. The simulator models an arbitrary number of mobile robots in a 2D environment, each controlled by a arbitrary algorithm. -
22
ExSTraCS
Extended Supervised Tracking and Classifying System
This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed... -
23
Unsupervised Random Forest
On-line Unsupervised Random Forest
This tool uses Random Forest and PAM to cluster observations and to calculate the dissimilarity between observations. It supports on-line prediction of new observations (no need to retrain); and supports datasets that contain both continuous (e.g. CPU load) and categorical (e.g. VM instance type) features. In particular, we use an unsupervised formulation of the Random Forest algorithm to calculate similarities and provide them as input to a clustering algorithm. For the sake of efficiency... -
24
Neural Libs
Neural network library for developers
This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network. -
25
fitGCP
Fitting genome coverage distributions with mixture models
Genome coverage, the number of sequencing reads mapped to a position in a genome, is an insightful indicator of irregularities within sequencing experiments. While the average genome coverage is frequently used within algorithms in computational genomics, the complete information available in coverage profiles (i.e. histograms over all coverages) is currently not exploited to its full extent. Thus, biases such as fragmented or erroneous reference genomes often remain unaccounted for. Making...
