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Description

Codebashing serves as Checkmarx’s innovative eLearning platform that enhances developers' abilities to address vulnerabilities and produce secure code. Building on the principle of experiential learning, Codebashing instructs developers on secure coding practices while honing their application security expertise in the most productive manner. Equip your developers with the essential skills needed to bolster security and mitigate risks from the outset. Transition developer security training into a continuous journey that seamlessly integrates into everyday tasks, ensuring that learning is ongoing, tailored, and directly meets the changing demands of developers. Custom-designed secure coding training pathways are meticulously created to provide developers with knowledge pertinent to their specific roles, ensuring that security instruction is both relevant and impactful. This tailored educational experience comprises 85 lessons that address every facet of the Software Development Life Cycle (SDLC), aiming to empower security-conscious developers to emerge as security advocates within your organization. Ultimately, Codebashing not only builds individual skills but also fosters a culture of security awareness throughout the development team.

Description

TabFM is an innovative zero-shot foundation model specifically created for handling tabular data, aimed at streamlining classification and regression processes that usually necessitate extensive manual model training, hyperparameter optimization, and tailored feature engineering. By transforming the challenge of tabular prediction into an in-context learning task, TabFM avoids the need to train a new supervised model for every dataset; instead, it consolidates historical training examples and target testing rows into a single cohesive prompt, allowing it to discern the intricate relationships between various columns and rows during inference. Given that tables are inherently two-dimensional and do not rely on a specific order, TabFM employs a hybrid architecture that integrates alternating attention mechanisms for both rows and columns, row compression techniques, and a specialized Transformer designed for in-context learning based on these compressed row embeddings. This sophisticated framework enables the model to effectively capture complex interactions and dependencies among features while maintaining computational efficiency, particularly advantageous for processing larger datasets. Furthermore, this approach not only enhances performance but also significantly reduces the time and resources typically required for model development in tabular data tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

.NET
Android
Apple iOS
C
C++
Go
Java
Kotlin
Node.js
PHP
Python
Ruby
Scala
Swift

Integrations

.NET
Android
Apple iOS
C
C++
Go
Java
Kotlin
Node.js
PHP
Python
Ruby
Scala
Swift

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Checkmarx

Country

United States

Website

checkmarx.com/glossary/codebashing-definition/

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Product Features

Product Features

Alternatives

Alternatives

No Alternatives
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