Deep learning is an area of machine learning algorithms having multiple layers for feature extraction and transformation, each successive layer uses output from previous layer as an input. Deep learning includes learning of deep structured and unstructured representation of data and allow to build a solution optimized from algorithm to solve machine learning problems. It is fastest-growing field in machine learning using deep neural networks to abstract data such as images, sound, and text. Thus deep learning has become growing trend in Machine learning. To abstract better results when data is large and complex.
Advancement in machine learning, artificial intelligence and deep learning helps in many ways in our daily life. Machine helps in most logic and rule-based systems designed to solve problems by using a suite of algorithms to go through data to make and improve decision making process. But within machine learning deep learning make sense of data using multiple layers of abstraction. As many companies across industries seek to use advanced computational techniques to find useful information hidden across huge data. Thus deep learning can create intelligent, self-learning machines which gave easiness to complex systems.
Works as a framework for machine learning which solves complex problems easily using techniques like convolutional neural network, recurrent neural network. Many tools make it beneficial like natural language processing software tool helps the computer decipher messages or text, Image recognition software tool enables the computer to search, sort, and segment for object detection and speech recognition software tool allows humans to interact with their smart gadgets. It provides simplicity, accuracy, flexibility with expert system.
In present Scenario, clients demand real time application, to process huge amount of data of business firms and blue chip stocks deep learning is used. Deep learning is a key to learn from unstructured data beneficial in real-world applications whose networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction. Helps researchers analyze medical data to treat diseases, enhances doctors’ to analyze medical images thus improving patient care.
Webtunix is an artificial intelligent based platform, able to deliver solutions to improve and enhance various programs that uses deep learning networks to detect, predict and prevent advanced persistent threats in real time. AI has been focused on deep learning, which include training artificial neural networks on lots of data and then getting them to make inferences about new data. In classification of different diseases, support vector machine and machine learning is used,
Webtunix is one of the leading company in machine learning, artificial intelligence and deep learning. Our expertise handle project based on deep learning and AI able to design, train, deploy neural network and image processing using feature extraction. Further solve project over deep neural network model, fingerprint matching, image recognition, age estimation, word recognition, mapping, removal of high density noise using fuzzy filters. All of these is possible by a family of AI technique known as deep learning, though deep neural networks. Vision is to transform industry through deep learning technique with increasing percentage of accuracy. Performance in not just accuracy but to provide services.
Webtunix Solutions is leading deep learning based an Artificial Intelligence Company in India. Our fortune 500+ clients rely on us to improved business outcomes, operational efficiencies and drive innovation. Our Intelligent solutions provide industry-leading machine learning, natural language processing, analytics and knowledge graphing capabilities at scale.
History of Neural Networks, Introduction to Deep Learning Theory, Introduction to Deep Learning and Neural Networks.
NumPY/SciKit Learn basics.
Introduction to Tensorflow and Theano.
Introduction to Keras, demonstration of Neural Network, building a basic Neural Network.
Neural network internals, activation functions, backpropagation, loss functions, weight inilialization.
Data normalization/Standardization, model tuning, deployment, and scaling, Deep Network topologies, feed Forward
How to Choose an Appropriate Neural Network
Tuning, overfitting, learning rate, adaptive learning rates, dropout, regularization
Advanced topics, import Keras into deeplearning4j for production, model Import,transfer learning, model serializer.