Uncover the hidden potential of complex data by ordering our custom deep learning development services and equip your business with the most advanced competitive edge. Our team’s competence is grounded on extensive commercial experience of 40+ implemented projects, knowledge of deep learning frameworks and libraries, as well as a strong academic background with Masters or Ph.D. degrees in Computer Sciences and Applied Math.
Our qualification is backed up with:
Deep learning is a subfield of machine learning, where computer machines perform tasks that normally involve human intelligence. To be able to accomplish such tasks artificial neural networks need to learn from vast sets of data. By learning from experience, multiple layers of algorithms repeat the tasks over and over and eventually improve outcomes. With deep learning, machines acquire capabilities to solve very complex problems and get insights from data that is messy, unstructured, and diverse. Deep learning models are able to deliver more accurate results without the help of humans. This opens up a promising perspective for their use in business applications.
Our team possesses decade-long experience in data Science, custom R&D, and software development. Being well versed in such technologies as Tensorflow, PyTorch, BERT, Theano, Opencv, Keras, and SpaCy, our experts can implement programmable neural networks into custom software applications to solve real-life business problems.
We develop highly-efficient and scalable solutions to extract live data from images and videos that involve plain Computer Vision, image recognition, OCR methods, and Deep Learning.
One of our primary fields of competency involves developing deep learning algorithms capable of object detection, human identification, facial recognition, and emotion recognition.
We have solid experience solving problems related to speech recognition, natural language understanding, natural language generation, machine translation, Named Entity Recognition (NER), and Optical Character Recognition (OCR).
Our experts have solid competence in building Predictive Analytics models for customer analysis, analysis of IoT signals and energy consumption, and other fields, helping to identify the likelihood of future outcomes based on historical data.
Appling powerful neural networks to integrate speech recognition capabilities into software applications, including conversion of written text into voice, and vice versa.
Mediterra offers its team’s expertise to perform an audit of your existing project, analyze algorithms and approaches applied, and suggest improvements. If you are only considering deep learning options, we can help assess the possibilities to integrate DL into your business processes.
We have a long record of delivering successful projects in Finances, Sports, Telecommunications, Agriculture, Engineering, Retail, and other industries.View All
Our client breeds elite racing horses with excellent physical characteristics. The project objective was to build an application that processes videos of a specific horse and calculates the probability of this horse being elite. We created machine learning algorithms and models based on provided data such as horse heart rate, weight, body size, cardiac index, ultrasound/echo, and DNA. At the next step, we built a database and web application that manages current and new data about horses and predicts eliteness. Then we created Web Jobs for automatic retraining of the machine learning models. As a result, we delivered a web app that proves a correlation with more than 80% accuracy for elite thoroughbred horses. To implement the project, we utilized Python and Django for the web app and OpenCV with Neural networks for the Machine Learning module.Learn more
The business goal of the project was to build a system that parses resumes and jobs and finds matches between specific job descriptions and applicants’ resumes based on age, gender, educational and professional background. The parsing part involves a complex solution based on the Apache Tika toolkit, ontologies (for skills, cities, universities, and so on), and NLP techniques. For matching, the system uses machine learning algorithms utilizing different information sets extracted from resumes and job systems. The trained model system ranks custom resumes/jobs and finds top N jobs/resumes that bring together the most matches. The system has an interface for retraining matching models based on new or updated resumes and jobs. Also, the system provides a RESTful API interface that allows external systems to use its functionality.Learn more
The goal of the project was to find a correlation between market news about a company and related stock prices. In order to do this, we have defined a list of stocks and scrapped news about corresponding companies. For each news found, the system analyzes if there is a stock price change that could be related to this news. The text of news is analyzed using different algorithms, including sentiment analysis, bag-of-words, catboost, regression, and others in order to detect if there is any specific content/fact that could lead to price change. Based on analysis results we built a regression model that matches relevant stock prices with certain market news/events and based on this model tries to predict if specific news would influence the stock price, how big this change would be, and if it would be negative or positive.
The project focuses on finding, extracting, and structuring information from different data sources, including data directories, APIs, and corporate websites. This extracted information is different and could be defined by user schema. For example, the system browses all pages of a corporate website to find and retrieve such information as the company name, location, industry, name, and position of employees as well as other interested parties. As the output for each website, the system generates a well-defined and structured JSON object containing all extracted information in a clear and easy to process format. In order to cover as much useful information as possible, we retrieve it from various ypes of data (plain text, tags, tables, forms, and images) using such approaches and state-of-the-art solutions as ML, NLP, NER, NRE, BERT, OCR and others.
A budget-friendly and low-risk model used when the scope and requirements of the project are well defined and documented.
Better suited for projects with less clear specifications and timelines, and provides the possibility to balance team size, as well as project workloads.
This model is used for long-term contracts, where a dedicated team of specialists is allocated for the project with an agreed fixed monthly payment for the engaged human resources.