Search Results for "reverse"
Sort By:
Showing 14 open source projects for "reverse"
View related business solutions-
Deploy Apps in Seconds with Cloud RunCloud Run is the fastest way to deploy containerized apps. Push your code in Go, Python, Node.js, Java, or any language and Cloud Run builds and deploys it automatically. Get fast autoscaling, pay only when your code runs, and skip the infrastructure headaches. Two million requests free per month. And new customers get $300 in free credit.
-
Ship AI Apps Faster with Vertex AIShip AI apps and features faster with Vertex AI—your end-to-end AI platform. Access Gemini 3 and 200+ foundation models, fine-tune for your needs, and deploy with enterprise-grade MLOps. Build chatbots, agents, or custom models. New customers get $300 in free credit.
-
1
ChainRulesCore
AD-backend agnostic system defining custom forward and reverse rules
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system. The ChainRulesCore package provides a light-weight dependency for defining sensitivities for functions in your packages, without you needing to depend on ChainRules itself. This will allow your package to be used with ChainRules.jl, which aims to provide a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse-, and mixed-mode primitives. -
2
ChainRules.jl
Forward and reverse mode automatic differentiation primitives
The ChainRules package provides a variety of common utilities that can be used by downstream automatic differentiation (AD) tools to define and execute forward-, reverse--, and mixed-mode primitives. The core logic of ChainRules is implemented in ChainRulesCore.jl. To add ChainRules support to your package, by defining new rules or frules, you only need to depend on the very light-weight package ChainRulesCore.jl. This repository contains ChainRules.jl, which is what people actually use directly. ... -
3
ReverseDiff
Reverse Mode Automatic Differentiation for Julia
ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy. -
4
ForwardDiff.jl
Forward Mode Automatic Differentiation for Julia
...While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms (such as finite-differencing) in both speed and accuracy. Functions like f which map a vector to a scalar are the best case for reverse-mode automatic differentiation, but ForwardDiff may still be a good choice if x is not too large, as it is much simpler. The best case for forward-mode differentiation is a function that maps a scalar to a vector. -
Go from Data Warehouse to Data and AI platform with BigQueryBigQuery is more than a data warehouse—it's an autonomous data-to-AI platform. Use familiar SQL to train ML models, run time-series forecasts, and generate AI-powered insights with native Gemini integration. Built-in agents handle data engineering and data science workflows automatically. Get $300 in free credit, query 1 TB, and store 10 GB free monthly.
-
5
JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling... -
6
Java 2D & 3D visual entity relationship design & modeling (ERD,SQL) for Oracle,MSSQL,Postgres,MySQL,...,Database change&dictionary management, Swing Data Binding, Apache FOP Renderer for dot matrix printers,Sparx Enterprise Architect Reports + more
-
7
Jupyter Dock
Perform molecular docking protocols interactively
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results. These notebooks are Python 3 compatible. Each protocol and Jupyter notebook has its own test folder for testing and reproducibility evaluation. For all notebooks, the demonstration includes the use of AutoDock Vina and Ledock. When available, some alternatives are mentioned in the protocol. Jupyter Dock's initial goal... -
8
Open Source Data Quality and Profiling
World's first open source data quality & data preparation project
This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy. This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data... -
9
Zika-RNAseq-Pipeline
An open RNA-Seq data analysis pipeline tutorial
...The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
10
PRADA
PRADA : Pipeline for RNA-Sequencing Data Analysis
Massively parallel sequencing of cDNA reverse transcribed from RNA (RNASeq) provides an accurate estimate of the quantity and composition of mRNAs. To characterize the transcriptome through the analysis of RNA-seq data, we developed PRADA. PRADA focuses on the processing and analysis of gene expression estimates, supervised and unsupervised gene fusion identification, and supervised intragenic deletion identification. -
11
A tool that parses SQL Select statements and generates a diagram. The diagram shows parts of the underlying SQL directly in the diagram. For example x=30 , GROUP BY (year), HAVING MIN(age) > 18. It is easy to see cartesian joins and/or loops.
-
12
A generic SQL driven data audit tool for detecting differences between any JDBC accessible database tables and other data sources. Platform independent. It's a unix like diff for databases. Produces key values with the differing column name and data
-
13
Cscape is an application for visualizing and navigating through data from a Reverse Phase Protein Microarray (RPMA). Cscape uses the Google Maps API to map the RPMA data on top of an image of a cancer cell and its associated pathways.
-
14
JRET (Java Reverse-Engineering Tool) is a program that is capable of reconstructing sequence diagrams from JUnit testsuites through dynamic analysis. It is written in Java/AspectJ and provides a GUI as well as command-line possibilities.
- Previous
- You're on page 1
- Next
