We use TensorFlow for Machine Learning and Deep Learning projects.
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:
This concerns 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 digitization of text, typed or even handwritten. Successfully used to digitize paper documents, such as filled forms and invoices.
The implementation cases of pattern recognition features are endless: from the data analytics tasks (banking transactions patterns detecting), to industries changing media data processing (satellite images, industrial camera feeds analytics).
Analyses user behavior data or purchase history and compares it to similar users. You can up sales 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 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 whilst taking into account a complex knowledge base.
This concerns 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 digitization of text, typed or even handwritten. Successfully used to digitize paper documents, such as filled forms and invoices.
The implementation cases of pattern recognition features are endless: from the data analytics tasks (banking transactions patterns detecting), to industries changing media data processing (satellite images, industrial camera feeds analytics).
Analyses user behavior data or purchase history and compares it to similar users. You can up sales 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 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 whilst taking into account a complex knowledge base.