Reverse Dependencies of keras
The following projects have a declared dependency on keras:
- sc-permut — Deep learning annotation of cell-types with permutation inforced autoencoder
- scam-net-rewintous — Score Weighted Class Activation Mapping. A tool for convolutional neural network activation analysis
- scbean — integration
- schzz — A way to make ml posts in my website
- sci-mls — The friendly scientific machine learning library
- scikeras — Scikit-Learn API wrapper for Keras.
- scikinC — A converter for scikit learn and keras to hardcoded C function
- scikit-multilearn-ng — Scikit-multilearn-ng is the follow up to scikit-multilearn, a BSD-licensed library for multi-label classification that is built on top of the well-known scikit-learn ecosystem.
- scivae — no summary
- sclera — One-stop destination for Computer Vision with Keras
- scmvae — a comprehensive single-cell multimodal analysis python package based on mixed variational autoencoder
- scpy4reactome — python service for single cell analysis in Reactome
- sctransfer — Python part for scRNA-seq transfer learning denoising tool SAVER-X
- sd-nn — implementation of the source differential neural network.
- segmentation-models-3D — Set of Keras models for segmentation of 3D volumes .
- SegSRGAN — Segmentation and super resolution GAN network
- SeisMonitor — To monitor seismic activity
- seldon — Seldon Python Utilities
- sem-reject — sem_reject
- sentiment-analysis-csci-e89 — Package for end to end setiment analysis using Neural Architectures
- seq2annotation — seq2annotation
- seq2vec — A package to turn sequence of words into a fix-length representation vector
- seqeval — Testing framework for sequence labeling
- seqtag-keras — Easy to use BiLSTM+CRF sequence tagging for text.
- sequential-ft-transformer — FT Transformer applied to sequential tabular data
- shap — A unified approach to explain the output of any machine learning model.
- shapeae — no summary
- shapr — no summary
- shatter — Data Driven Programming
- shinherpro — shinher-pro 1.7.3
- SHiNiNG — The easiest and powerful deep-learning-text-classifier based on keras and gensim for human beings and all purposes.
- shrinemaiden — An auxiliary library to help process data for ML/DL purposes
- signs — Signs Text Processing for Deep Learning
- silicon-analyser — helps to analyse integrated circuit die images (for example from siliconpr0n.org) with the help of ai.
- simbolo-mpst — Simbolo Multilingual Partial-syllable Tokenizer
- simbolotokenizer — Multilingual Partial Syllable Tokenization - A rule-based tokenization method designed to align with linguistic nuances while minimizing False Positive errors.
- simhandler — Intelligent simulation handler for power system load flow simulations
- simmpst — Simbolo Multilingual Partial-syllable Tokenizer
- simnets — SimNets implementation in tensorflow
- simple-co-train — A simple co-training library built on Keras.
- singularitytechnologies.easymodelskeras — Easy to use Keras Machine Learning Model
- SixAdsDS — Generic functions for SixAds data science projects
- sizif — Deep learning Keras models lifecycle management backup/restore nano framework
- skeraton — Keras implementation of skeleton transformer module
- skil — Train, deploy and manage your Python models with SKIL
- sklearn-sequence-classifiers — Sequence classifiers for scikit-learn
- sktime-dl — Deep learning extension package for sktime, a scikit-learn compatible toolbox for learning with time series data
- skywatchai — An API Wrapper for powerful face detection, verification and recongition for python
- smart-app-framework — Python-фреймворк, который позволяет создавать смартапы для виртуальных ассистентов Салют.
- SmartAnno — A smart snippet annotation tool with deep learning backbone.
- smashpy — SMaSH: A scalable, general marker gene identification framework for single-cell RNA sequencing and Spatial Transcriptomics
- smeagol-bio — no summary
- smenan — Developed for Sade Tech. Corp.
- smic — Image Classification library built on top of Keras. Identifies the best set of hyperparameters and trains a classification model accordingly, hence, smart.
- smote-variants — Variants of the synthetic minority oversampling technique (SMOTE) for imbalanced learning
- sng — Generate name proposals for companies, software, etc.
- snvoter — A top up tool to enhance SNV calling from Nanopore sequencing data.
- social-net-img-classifier — no summary
- SODBASNET — BASNET model created using tensorflow.
- sokoban-rl — Application of reinforcement learning to the sokoban game
- sonusai — Framework for building deep neural network models for sound, speech, and voice AI
- soph — Tools I find useful
- sourced-ml — Framework for machine learning on source code. Provides API and tools to train and use models based on source code features extracted from Babelfish's UASTs.
- sourced-ml-core — Library containing the core algorithms for machine learning on source code. Provides API and tools to train and use models based on source code features extracted from Babelfish's UASTs.
- spacy-combo — COMBO wrapper for spaCy
- SpaDecon — SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning
- sparsely-connected-keras — Sparsely-connected layers for Keras
- sparx-lib — Sparx Implementation
- sphinx-summaries — no summary
- sputummrcnn — Custom Mask RCNN
- starttf — A tensorflow batteries included kit to write tensorflow networks from scratch or use existing ones.
- stock-analysis-engine — Backtest 1000s of minute-by-minute trading algorithms. Automated pricing data ingestion from: IEX Cloud (https://iexcloud.io), Tradier (https://tradier.com) and FinViz. Datasets and trading performance automatically compressed and published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes with Helm and docker-compose. >150 million trading history rows generated from +5000 algorithms
- stockDL — Predicts the Gross Yield, Annual Yield and Net Yield of a user given stock ticker.
- stokenizer — Multilingual Partial Syllable Tokenization - A rule-based tokenization method designed to align with linguistic nuances while minimizing False Positive errors.
- stos — Converting the American sign language into speech or text, and vice versa.
- strainmap — no summary
- stuned — Utility code from STAI (https://scalabletrustworthyai.github.io/)
- stylometry-utils — Collection of functions and utilities to run stylometry experiments
- subaligner — Automatically synchronize and translate subtitles, or create new ones by transcribing, using pre-trained DNNs, Forced Alignments and Transformers.
- SubBrainSegment — Package for subcortical brain segmentation
- sumo-experiments — The sumo-experiments library implements a python interface for the Simulation of Urban MObility (SUMO) software.
- sumonet — Package Description
- superraenn — Python package to classify supernovae based on optical light curves
- SupervisedGRN — This package leverages both supervised machine learning and deep learning techniques to construct robust predictive models from transcriptomic data. It is designed to train on this high-dimensional data, extract key features, and make accurate predictions. The models generated can be used to uncover complex biological relationships and predict outcomes such as gene regulatory network behaviors, offering valuable insights for genomics and bioinformatics research.
- svlearn — Utils for ML
- svrg-optimizer-keras — SVGR optimizer for Keras
- syngen — The tool uncovers patterns, trends, and correlations hidden within your production datasets.
- synth-data-metrics — Synthetic Data Metrics is a Python library for evaluating synthetic data quality across a wide range of data types (image, tabular, time series, language) and approaches to evaluation.
- syntheseus-paroutes — Syntheseus wrapper for PaRoutes benchmark.
- synthetic-data-metrics — Synthetic Data Metrics is a Python library for evaluating synthetic data quality across a wide range of data types (image, tabular, time series, language) and approaches to evaluation.
- szurubooru-toolkit — Python package and script collection to manage szurubooru.
- table15 — Table 1.5 is a Python application that can generate a table that is adjunct to a typical Table 1 (association statistics). Table 1.5 goes beyond static association by analyzing the impact that a change in each single feature has to changes in the outcome.
- tatau — no summary
- taug — Time Series Forecasting and Data Augmentation using Deep Generative Models
- tcsa — temporalis segmentation pipeline to assess CSA of temporalis muscle
- TelescopeML — An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results
- tensor-evolution — Evolutionary algorithm for neural network structure
- tensorcross — Cross Validation, Grid Search and Random Search for TensorFlow Datasets.
- tensorflow-ml — Tensorflow ML
- tensorfree — Tensorfree is an image classification library that provides quick and easy access to some of the latest SOTA models. Simply install, define the location of your photos and let it do everything for you.