Innovative TensorFlow Applications for Machine Learning Models

Innovative TensorFlow Applications for Machine Learning Models

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...

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. More than just switching appliances on and off, AI-powered home automation can enable users to monitor temperature, lightning, energy-consumption, and more.

b) Speech-to-text translation models
The first revolutionary implementation of speech-to-text models was brought to the masses by Apple’s Siri. The idea of deploying neural networks for speech recognition gave a big break to deep learning technology.

Unlike traditional voice recognition methods, deep learning uses Recurrent Neural Networks (RNN) that are efficient at mapping an audio file’s sequences. However, the key to easier and faster implementation of speech recognition models lies in Google’s TensorFlow library. The open-source machine learning library provides a flexible, scalable, and highly adaptable interface for building speech recognition models.

speech recognition rnn ai

Source- Slideshare

Here’s how businesses can embed speech recognition models for different applications-

a) Voice commands for automated bill payment

b) Scheduling social media marketing campaigns

c) Voice-controlled eCommerce purchases and recommendations

Also Read- Deploying TensorFlow and Keras for Deep Learning Models

How does Oodles AI deploy TensorFlow to build speech recognition models?
We, at Oodles, have hands-on experience in NLP technologies to train TensorFlow models for speech and image recognition. Our AI team uses Convolutional Neural Networks (CNN), the histogram of oriented gradients (HOG) to perform accurate and efficient deep video analytics for real-time image recognition. For speech recognition, we use the combination of RNN and TensorFlow to build the following AI solutions-

a) Our capabilities include voice-controlled home automation applications powered by Alexa and Google Voice. We use AI’s deep neural networks to automate household IoT devices for instant voice calling, push notifications, and virtual assistant support.

b) Our team has recently built a TensorFlow-based word recognition model that translates speech and audio files into written texts. The model can be deployed for multiple business applications such as eCommerce product searches, business calendar updates, conference call management, and more.

  1. Predictive Analytics using TensorFlow
    Accuracy of predictions is the most critical factor in making well-informed business decisions. With the advent of artificial intelligence, machines are overcoming human limitations to make accurate and timely predictions for diverse businesses.

The process of generating predictions requires machines to analyze tons of structured and unstructured data to identify patterns and behavior. The high computational capacity of TensorFlow makes it ideal for parallel processing of data. It enables SMBs to derive valuable insights with predictive analytics even with limited datasets using methods like linear regression.

Following are some innovative business applications of TensorFlow-based predictive analytics-

a) Aiding medical diagnosis
By using Electronic Health Records (EHR), predictive engines can anticipate the condition of certain chronic diseases among patients. Currently, predictive analytics can be applied to assess heart risk, death risk, and other infectious diseases by predicting epidemic disease dynamics.

b) Boosting agriculture output
Predictive analytics has already entered the agricultural fields to automate demand, yield, and crop health prediction. However, the applications of AI-powered predictive engines are now extending to detect certain plant diseases using TensorFlow libraries.

Also Read- How Artificial Intelligence in Healthcare is Redesigning Treatment

Optimizing TensorFlow Applications with Oodles AI
The AI team at Oodles has a working knowledge of deploying predictive analytics for multiple business applications. Our most recent POC development involved a Diabetic Prediction System using two-boost algorithms aHow Artificial Intelligence in Healthcare is Redesigning Treatmentnd libraries such as TensorFlow, Scikit-Learn, and Flask. The model is trained using structured data inputs like Plasma Glucose, Tricep Thickness, Blood Pressure, and other medical details to draw diabetic predictions.

predictive analytics healthcare ai tensorflow

The model’s USP is that it does not require any intervention of a physician to measure a person’s diabetes. The system works on a simplified interface that enables common individuals to test diabetes easily and accurately.

In addition, our AI experts are working on a cloud-based ML model that predicts the presence of specific resources under the earth’s surface. The model is trained using various libraries and sensor-based drilling machine data. The primary objective of our team is to empower global businesses with artificial intelligence technologies to accelerate innovation and growth.

Talk to our AI development team to know more about our artificial intelligence services.

artificial intelligence services

artificial intelligence services

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Learning in Artificial Intelligence - Great Learning

Learning in Artificial Intelligence - Great Learning

What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?

US and China are massively investing in Artificial Intelligence which create a promising career in the field. One of the first steps to a successful artificial Intelligence career is to learn the basics around the domain. Articles and Guides are your opening friends towards a successful AI Career. Read on to know more.

Artificial Intelligence: An Overview of Its Applications and Use-Cases

Artificial Intelligence: An Overview of Its Applications and Use-Cases

AI (artificial intelligence) is the simulation of the human brain’s intelligence processed by machines, especially computer systems. It processes include information and rules for using the information and using rules to reach approximate or...

AI (artificial intelligence) is the simulation of the human brain’s intelligence processed by machines, especially computer systems. It processes include information and rules for using the information and using rules to reach approximate or definite conclusions plus self-correction. Artificial intelligence is a branch of computer science that aims to create smart and intelligent machines that work and react like humans. It has become a vital part of the information technology industry.

In today’s world, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding. AI development services are helping industries to solve many challenges in software engineering. Particular applications of AI include expert systems, speech recognition, operations research. and machine vision.

Examples of AI Technology
AI is involved in various types of technology. Below are examples.

Automation: Enables a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repetitive tasks that humans normally performed taking more time.

Machine vision: The science that allows computers to see by capturing and analyzing visual information using a camera, analog-to-digital conversion, and digital signal processing. It can be compared to human eyesight, but machine vision isn’t bound by biology and can be programmed to see through walls, for instance. It is used in signature identification for medical image analysis. Computer vision is focused on machine-based image processing.

Natural language processing (NLP): The processing of human language by a computer program. One of the best-known applications of NLP is spam detection, which looks at the subject line and the text of an email and decides if it’s junk. Current approaches to NLP are based on machine learning and its various tasks include text translation, sentiment analysis, and speech recognition.

Applications of Artificial Intelligence
Artificial intelligence has made its application in many areas. below are some examples.

Healthcare. Companies are applying artificial intelligence to make better and faster diagnoses than humans for improving patient outcomes and reducing costs. For instance healthcare technology IBM Watson understands natural language and is capable of responding to questions asked of it. The system mines patient data to form a hypothesis, which it then presents with a confidence scoring schema.

Business.AI in Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning development services are providing solutions to integrate algorithms into analytics platforms to uncover information on how to serve customers better. Chatbots have been incorporated into websites to provide immediate service to customers along with the Automation of job positions.

Finance. AI in personal finance applications, such as Mint or Turbo Tax collect personal data and provide financial advice. Other technologies such as IBM Watson, have been applied to the process of buying a home.

Law. The discovery process, sifting through documents, in law is often tedious for humans. Automating this process is a more efficient use of time and less human efforts.

Manufacturing. Industrial robots are being used to perform single tasks and were separated from human workers, but as technology advanced that changed.