Introduction to Deep Learning Frameworks and Application ERA

Deep learning is a machine learning technique that teaches computers to learn by mimicking human learning processes. Deep learning is a crucial component of driverless automobiles' ability to recognise stop signs and distinguish between pedestrians and lampposts. Deep learning (DL) frameworks offer the building blocks for creating, educating, and assessing deep neural networks via a high-level programming interface. Four different framework types exist the linear automation framework, the modular-driven framework, the behaviour-driven framework, the data-driven framework, and the hybrid testing framework. The widespread applications include 1) Customer relationship management systems and 2) Fraud detection. 3-D computer imaging 4) Voice-activated AI 5) NLP 6) Data repurposing 7) Autonomous cars 8) Supercomputers. In this chapter we are going to learn 1.) TensorFlow, 2.) PyTorch, 3) Keras, 4) Sonnet 5) MXNet 6) Swift for TensorFlow 7) Gluon 8) DL4J 9) ONNX 10) Chainer and deep learning applications.