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Deep Learning with Keras

Deep Learning with Keras

Deep learning is the subset of a family of machine learning methods based on artificial neural networks. Deep learning architectures like deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied in the fields of computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced good results comparable to and in few cases superior to human experts. A deep neural network provides accuracy in many tasks, from object detection to speech recognition. They can learn automatically, without any predefined knowledge and these are exclusively coded by the programmers. Keras is a high-level neural networks API, which can run on top of others. It facilitates fast experimentation through a high level, user-friendly, modular and extensible API. Keras is a user-friendly neural network library written in Python.

A Deep Learning Specialist focuses on machine learning tools and deploying them to solve problems by making decisions. With the use of deep learning, data is processed through neural networks, getting closer to how we think as humans. Deep learning can be applied to images, text, and speech to draw conclusions that will be the same as human decision making. To do this, one use neural networks, which are data structures similar to human brain structure. Although deep learning is considered a subset of machine learning, it is more sophisticated. The objective is that the machines operating with deep learning must be able to operate independently without human aid.
Deep learning research engineers are involved in developing system and program design plans and determine how to integrate machines and programs. They are responsible for developing systems that can transfer data effectively and write complex computer programming code to direct parts of the neural network to operate in a proper manner. They must specifically work on things like self-driving vehicles, facial recognition software and robots to keep themselves up to date.

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