Deep Learning

Deep Learning is one of the sub-branch of Artifical Intelligence. Deep Learning techniques use Artificial neural networks to train a model and provides intelligent models which think similar to humans. Deep Learning is gaining a lot of attention in recent days due to the ever-changing behavior of people and it is becoming difficult to understand the patterns using traditional Machine Learning approaches, Deep Learning’s multi-layer training technique understands the patterns by diving deep into your data. As the size of the data increases more the need for Deep Learning increases.

Our team at PRONIX has dominant expertise in Deep Learning and Neural networks, which lets our customers see the way to success deriving actionable results by applying Deep Learning techniques on Big Data.

As like Machine Learning, Deep Learning can also be classified into three Supervised Learning, Unsupervised Learning & Reinforcement Learning. Concepts of both Machine Learning & Deep Learning looks the same, but they differ in terms of understanding the complexity and learning from your data to train a model in their own way.

Deep Learning works on the principles and architectures of Artifical Neural Networks, there are different types of Artificial Neural Networks among them CNN & RNN architectures are widely used to solve the traditional Deep Learning problems.

CNN-  CNN stands for Convolutional Neural Network, CNN models are widely used to perform image recognition and image classification. CNN models take an image as input and run it through its convolutional layers to classify the image correctly. CNN also gained its significance in text data classification and sentiment analysis.

RNN- RNN stands for Recurrent Neural Networks, RNN models are widely used when the output of a prediction is dependent on the sequence of input predictions. RNN models not just consider the current prediction but also keeps the track of its previous predictions to decide the output of the current prediction. Speech recognition application is a classic example of RNN.

We provide expert solutions to our clients with our proficiency in the proven Deep Learning libraries Tensorflow, Keras, Theano, PyTorch TFLearn and many more.