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TensorFlow

We use TensorFlow for Machine Learning and Deep Learning projects.

TensorFlow Development Services

Being originally an open source lib, Tensorflow is a great tool used for training/deployment of ML (machine learning) models, performing Big Data tasks, identification of patterns, and solving Data Analytics challenges.

Now TensorFlow is more than a library but entire ecosystem, create to solve Deep Learning tasks that are similar to the tasks a human brain solves.

Currently TensorFlow supports many programming languages and launch reusable models on different platforms, including but not limited to browsers, mobile devices and IoT devices. However, the primary language of TensorFlow library is Python. Being a Python and TensorFlow development company, we know how to optimize the performance of the library, launching models on CPU(central processing unit) and GPU (Graphic processing unit) as well as on application-specific integrated circuits (ASIC), for example TPU (TensorFlow Processing Unit) from Google.

This in depth knowledge of the technology itself, it's primary language and possessing rare experience in ambitios implementation cases make us confident that we can provide high quality TensorFlow development services.

The two main reasons we focus TensorFlow for our development is:

  1. It's a completely established ecosystem.
  2. It's Python-friendly, Python native.

Some Features that Can be Implemented With TensorFlow

Face Recognition

The tasks related to the recognition of a human face. The ML model can be trained in a way to take into consideration different angles with occlusions as well as lighting conditions that normally affect standard comparison.

Handwriting Text Recognition OCR & ICR

The tasks related to the digitalization of text, typed or even handwritten. Successfully used to digitalize paper documents, such as filled forms and invoices.

Pattern Recognition

The implementation cases of pattern recognition features are endless: starting from the data analytics tasks (banking transactions patterns detecting), to industries changing media data processing (satellite images, industrial camera feeds analytics).

Personal Recommendation

Analyses user behaviour data or purchase history, compares it to similar users. You can up sale up to 30% by implementing personalized recommendations into your business.

Object Recognition

The tasks related to the recognition of objects and their types. The ML model is trained on the images with the same object in various lightning conditions, different angles and sometimes partially hidden. Great feature for retail (goods recognition) or security (license plate numbers recognition).

Image Generation

Makes edits or creates images from scratch taking into account a complex knowledge base.

The tasks related to the recognition of a human face. The ML model can be trained in a way to take into consideration different angles with occlusions as well as lighting conditions that normally affect standard comparison.

The tasks related to the digitalization of text, typed or even handwritten. Successfully used to digitalize paper documents, such as filled forms and invoices.

The implementation cases of pattern recognition features are endless: starting from the data analytics tasks (banking transactions patterns detecting), to industries changing media data processing (satellite images, industrial camera feeds analytics).

Analyses user behaviour data or purchase history, compares it to similar users. You can up sale up to 30% by implementing personalized recommendations into your business.

The tasks related to the recognition of objects and their types. The ML model is trained on the images with the same object in various lightning conditions, different angles and sometimes partially hidden. Great feature for retail (goods recognition) or security (license plate numbers recognition).

Makes edits or creates images from scratch taking into account a complex knowledge base.

Machine Learning and Deep Learning: What’s the difference?

In simple words the difference between these two can be explained as this:

  • If the task is quite specific, for example: "learn the dataset, detect patterns and based on this knowledge and certain parameters provide personal recommendation" - it is a machine learning task.
  • If the task is indefinite, like recognition of an object e.g. " make the camera recognize that it's pointed on a cup currently (and not a face for example)' - this is a deep learning task.

As a TensorFlow development company we are keen on working on new challenging projects in the field of artificial intelligence and machine learning.

Case

Business Intelligence
Platform for Marketing

A SaaS MVP based on web scraping, data analytics, and data visualization. Used to provide insights about competitors and market, provide pricing recommendations.

Case

Fleet Management
System

Business software for remoting management of workers, vehicles and routing optimization. Based on GPS tracking, orders distribution and Big Data. Web and Mobile system includes cloud backend.

Case

Risk Assessment
Analytics

A data analytics module that can forcast insurance risks based on the user parameters compared to the custom unique dataset of huge insurance history.

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