Artificial intelligence is proliferating new business grounds with impressive success. One of the key factors contributing to AI’s effective implementation is the availability of agile and dynamic libraries like TensorFlow. Today, SMBs can experience rapid AI integration by deploying various TensorFlow applications including speech and image recognition, predictive analytics, and more.

Let’s explore how businesses can merge disruptive artificial intelligence services with TensorFlow to build innovate machine learning models.

1) Speech Recognition Models

Traditional speech recognition techniques involved complex and time-consuming methodologies for audio feature extraction. The advent of machine learning technologies and supporting libraries such as TensorFlow has simplified speech recognition with pre-trained neural networks.

The underlying deep neural networks not only make data processing easier but also self-analyzes the inputs to improve model performance. With in-built support for multiple programming languages and neural networks, TensorFlow can contribute to the following business applications of speech recognition-

a) Voice-controlled home appliances and security systems

It is the most recent development in the speech recognition space. A combination of Internet of Things (IoT) devices and Natural Language Processing (NLP) techniques can create fully functional home automation systems.

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