The aim of cognitive computing is to mimic human thought processes in a computerized model. Using self-learning cognitive algorithms that use data mining, machine learning, pattern recognition, and natural language processing, the computer can imitate the way the human brain works.
Cognitive systems analyze the huge amount of data which is created by connected devices (not just the Internet Of Things) with diagnostic, predictive and prescriptive analytics tools which observe, learn and offer insights, suggestions and even automated actions.
Cognitive Computing and Machine learning addresses the challenge of passing the boundary of traditional data analytics algorithms, which spotlights the development of swift efficient cognitive algorithms.
These cognitive or machine learning algorithms enable real-time processing of huge volume of data, deliver precise predictions of various types such as recommending right products, customer segmentation, detecting fraud and risks, customer retention etc. Cognitive Computing and Machine learning supports these functions by creating a set of cognitive or machine learning algorithms that differ from the traditional statistical techniques. The emphasis is on real-time and highly scalable predictive/cognitive models, using fully automated methods that make data scientist tasks easier.
http://www.cognub.com/
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Artificial Neural Networks
Neural network projects typically use some of the few architectural approaches like RNN, CNN, NTM, LSTM.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts.
Neuroevolution is a subfield within artificial intelligence (AI) and machine learning (ML) that consists of trying to trigger an evolutionary process similar to the one that produced our brains, except inside a computer. In other words, neuroevolution seeks to develop the means of evolving neural networks through evolutionary algorithms.
Generative adversarial networks (GANs) are a class of neural networks more recently used in unsupervised machine learning. They facilitate a wide class of handy applications such as retrieving images that contain a given pattern or make predictions for a better medication to a certain disease.
APIs: Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Uses TensorFlow back-end engine by default.