Several intelligent tasks such as visual perception, language understanding, and auditory perception require construction of good internal representations of features. These features must be invariant to irrelevant variations of the input. These features must also preserve relevant information. A major obstacle in machine learning was to determine how to learn such good features automatically.

Convolutional neural networks use images directly as input. This network accomplishes the functions that are executed by cells in the visual cortex such as extracting elementary visual features like oriented edges, end-points, corners, etc.

Convolutional neural networks consist of convolutional layers which extract useful information from the input and eliminate irrelevant variability. Each stage in a convolutional network is composed of a filter bank, and feature pooling layers. With multiple stages, a convolutional network can learn multi-level hierarchies of features. Today convolutional neural networks are applied in a variety of areas, including natural language processing, image and pattern recognition, speech recognition, and video analysis.

Recurrent Neural Networks are a kind of artificial neural network aimed to identify patterns in sequences of data, such as text, handwriting, genomes, the spoken word, or numerical time’s series data emanating from sensors, stock markets and government agencies.

Recurrent Neural Networks (RNNs) are popular models that have shown great promise in many NLP tasks. But in spite of their recent status they only found a limited number of resources that thoroughly explain how RNNs work, and how to implement them. In a traditional neural network we assume that all inputs (and outputs) are independent of each other. But for many tasks that’s a very bad idea. If you want to expect the next word in a sentence you better know which words came before it. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being depended on the previous computations. The Alternative procedure to contemplate about RNNs is that they have a “memory” which releases information about what has been calculated so far.

Various Statistical and Mathematical Models are used to describe the behaviour of a physical system in terms of equations involving variables and parameters representing aspects of the system. For example, Newton's law of motion F = ma relates the acceleration a that an object of mass m experiences when subjected to a force F. They are significant in metrology as they agree to a value of one variable to be estimated from measurements of other variables. For example, in a mass spectrometer, the mass of a particle is estimated using the above law from measurements of F and a.

Brontobyte has a highly experienced team of technical experts in the area of mathematical and statistical modeling. We are part of a world-class physical metrology organisation, with experience and expertise in this field that is unique. We offer a range of services including advice, consultancy and training in the areas of modelling, experimental data analysis, and software development.

Predictive modeling is a procedure that uses probability and data mining to estimate outcomes. Each model is made up of a number of predictors, which are variables that are likely to influence future results. Once data has been collected for appropriate predictors, an arithmetic model is formulated. The model may employ a simple linear equation or it may be a complex neural network, mapped out by sophisticated software. As extra data becomes available, the arithmetic analysis model is validated or revised.

Predictive modeling is often associated with meteorology and weather forecasting, but it has many applications in business. Bayesian spam filters, for example, use predictive modeling to identify the probability that a given message is spam. In fake finding, predictive modeling is used to find outliers in a data set that point toward fraudulent activity. And in customer relationship management (CRM), predictive modeling is used to target messaging to those customers who are most likely to make a purchase.

Computers today have the power to ‘see’ and recognize what is happening in pictures. Our models can help notice objects such as cars, zoom in on regions of attention such as brand impressions, read text through character recognition and describe a scene through image captioning at world class accuracy levels.

Brontobype provides automated image analysis solutions using the field of Computer Vision and its different sub-domains such as object detection, recognition etc. Our Computer Vision work also includes the use of traditional techniques such as machine learning and Discrete Parts based Modeling along with the most recent Deep Learning approaches.

Applications built on Computer Vision or Machine Learning systems must be extremely perfect. While prudent usage of Machine/Deep Learning systems help you attain high classification accuracies, giving a human level perception capability to the system raises the usability of the system by a few more notches. Our solutions reach that by the meticulous fusion of contextual information with Deep Learning.

Natural-language processing (NLP) as a part of , artificial intelligence concerned with the interactions between computers and human (natural) languages . NLP essentially deals with programming computers to successfully process large natural language data. NLP sits at the intersection of computer science, artificial intelligence, and computational linguistic.

Word processor operations treat text like a mere sequence of symbols, but NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence and, ultimately, sentences convey ideas.

Processing of Natural Language is need when you want an intelligent system like robot to achieve as per your orders, when you want to hear choice from a dialogue based clinical expert system, etc.

Problems in natural-language processing frequently involve speech recognition, natural-language understanding, and natural-language generation.

Cognitive computing systems bring about the best of numerous technologies such as natural language queries and processing, real time computing, and machine learning based technologies. By using these technologies, cognitive computing systems can analyze unbelievable volume of both structured and unstructured data.

The purpose of cognitive computing is to imitate human thoughts and put it in a programmatic model for practical applications in related situations. This biggest name in cognitive computing – IBM Watson, relies on deep learning algorithms aided by neural networks. They work together to understand more data, learn more, and mimic human thinking better.