TensorFlow API

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<b>TensorFlow API: What It Is and Why It’s Needed</b>

TensorFlow API is an open-source library by Google designed for developing and training machine learning (ML) and artificial intelligence (AI) models. It supports languages like Python, C++, and Java, making it versatile for various specialists.

- Neural Networks: Supports deep learning for applications in computer vision, natural language processing, and more.

- Scalability: Enables distributed data processing across cloud servers and mobile devices.

- TensorFlow Lite and TensorFlow.js: Allow models to run on mobile devices and in browsers.

- Automatic Gradient Descent: Simplifies model tuning and optimization.

<b>What Problems Does TensorFlow API Solve?</b>

- Computer Vision: Image and video recognition.

- Text Processing: Text analysis, machine translation, and chatbots.

- Recommendation Systems: Content personalization.

- Time Series Forecasting: Financial predictions and trend analysis.

TensorFlow simplifies ML development, reduces development time, and allows models to be customized for business needs and deployed on various devices. For digital companies aiming to leverage AI capabilities to increase efficiency, TensorFlow offers a powerful and flexible solution.

With TensorFlow API, your company can create custom AI solutions that optimize operations and automate key processes.

Antifraud

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Protect your business and your profits by detecting fraudulent activity in real-time.&nbsp;

Maintain high pass rates with flows that are easy for users and impassable for fraudsters.

Detect fraudulent actions at every user step, not only during onboarding.

Get a complete picture of your customers risk profiles.

Track the whole user journey, from the moment they sign up until transaction completion. This helps you make informed decisions and keep your business safe.

Fraud continues to pose an increasing threat to the financial services industry.

Fraudsters are constantly enhancing their schemes to make detection by traditional fraud models more difficult. These shifting methods of attack ultimately result in institutions continuously having to allocate additional resources toward fraud, which requires a cohesive strategy that balances risk mitigation and client experience.

Developers, managers, data analysts, system administrators