The proliferation of artificial intelligence across businesses has amplified the development of dynamic deep learning models. What’s more, emerging neural network libraries such as Keras and TensorFlow development services are powering AI solutions with rich datasets. Together, these resources are enabling businesses to build effective and industry-specific image, text, and speech recognition systems with deep video analytics.
For better understanding, let’s explore some business benefits and deep learning applications powered by Tensorflow and Keras.
Decoding TensorFlow and Keras
TensorFlow is Google’s open-source library used extensively for building machine learning models and executing high-level numerical computations. Its agile architecture enables developers to streamline artificial intelligence services across GPUs, TPUs, Raspberry Pi, operating systems, and mobile interfaces.
tensorflow keras deep learning models
The complementary attributes of Keras and TensorFlow
Keras is an open-source neural network API written in Python and highly compatible with TensorFlow. The primary objective of both the neural network resources is to build deep learning models in a user-friendly and flexible manner. Keras and TensorFlow complement each other to support the development of high-octane facial and object recognition models.
Deep Learning Models with TensorFlow and Keras
a) Brand monitoring and expanded training
b) Facial gesture recognition
c) Mobile robotics
d) Visual Question Answering, and more.
A typical image recognition model requires rigorous training of its underlying machine learning algorithms with large datasets and patterns. The rich libraries of TensorFlow and Keras provide a modular design that processes the images in a layered fashion. It leads to intensive data training and highly accurate predictions.
How does Oodles AI use Keras and TensorFlow to build industry-specific deep learning models?
The AI development team at Oodles uses historical datasets and convolutional neural network (CNN) to train machine learning models with precision. The business applications of our computer vision capabilities using TensorFlow and Keras extend across the following industries-
a) Automated attendance systems for secure corporate infrastructures
b) Real-time object detection and video analytics for manufacturing businesses
c) 3D facial impressions in cars to prevent on-road accidents due to human lethargy
d) Facial recognition for eCommerce marketing campaigns backed by sentiment analysis.
Related- Optimizing the Code with Machine Learning for Software Development
Deep learning powers handwriting recognition systems that can strengthen business security in the following ways-
a) With optical character recognition (OCR) capabilities, data analysts can now use neural networks to interpret text variants from different locations.
b) It involves accurate predictions drawn from the handwritten and printed texts, signatures, house numbers, vehicle number plates, captchas, and more.
TensorFlow development services
Here’s a simplified visualization of how we deploy deep learning algorithms to build text recognition systems with TensorFlow’s accuracy and efficiency.
Also Read- Visualizing the Future of Computer Vision Across Businesses
Here’s how speech recognition systems are transforming key business operations-
a) Complete User analysis– Vocal inputs from users constitute the most valuable data for business development. With NLP, businesses can analyze user queries and complaints, demographic conditions, legal compliance, and social media interactions with accuracy and efficiency.
b) Wealth Management- The facilitation of phone banking has made speech recognition a mandatory technological update for financial institutions. Here, TensorFlow and Keras libraries enable banks to train ML models to generate accurate responses and insights.
Oodles OpenCV and TensorFlow Development Services
We, at Oodles, are empowering global businesses with AI and its underlying technologies like facial recognition, Natural language processing, predictive analytics, and more. We are adept at building data-driven machine learning models that extract key business insights from unstructured data, graphics, videos, and other web content.
Talk to our AI development team to know more about our artificial intelligence services.
#TensorFlow development services
The Association of Data Scientists (AdaSci), the premier global professional body of data science and ML practitioners, has announced a hands-on workshop on deep learning model deployment on February 6, Saturday.
Over the last few years, the applications of deep learning models have increased exponentially, with use cases ranging from automated driving, fraud detection, healthcare, voice assistants, machine translation and text generation.
Typically, when data scientists start machine learning model development, they mostly focus on the algorithms to use, feature engineering process, and hyperparameters to make the model more accurate. However, model deployment is the most critical step in the machine learning pipeline. As a matter of fact, models can only be beneficial to a business if deployed and managed correctly. Model deployment or management is probably the most under discussed topic.
In this workshop, the attendees get to learn about ML lifecycle, from gathering data to the deployment of models. Researchers and data scientists can build a pipeline to log and deploy machine learning models. Alongside, they will be able to learn about the challenges associated with machine learning models in production and handling different toolkits to track and monitor these models once deployed.
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We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.
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Welcome to DataFlair Keras Tutorial. This tutorial will introduce you to everything you need to know to get started with Keras. You will discover the characteristics, features, and various other properties of Keras. This article also explains the different neural network layers and the pre-trained models available in Keras. You will get the idea of how Keras makes it easier to try and experiment with new architectures in neural networks. And how Keras empowers new ideas and its implementation in a faster, efficient way.
Keras is an open-source deep learning framework developed in python. Developers favor Keras because it is user-friendly, modular, and extensible. Keras allows developers for fast experimentation with neural networks.
Keras is a high-level API and uses Tensorflow, Theano, or CNTK as its backend. It provides a very clean and easy way to create deep learning models.
Keras has the following characteristics:
The following major benefits of using Keras over other deep learning frameworks are:
Before installing TensorFlow, you should have one of its backends. We prefer you to install Tensorflow. Install Tensorflow and Keras using pip python package installer.
The basic data structure of Keras is model, it defines how to organize layers. A simple type of model is the Sequential model, a sequential way of adding layers. For more flexible architecture, Keras provides a Functional API. Functional API allows you to take multiple inputs and produce outputs.
It allows you to define more complex models.
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The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
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This video on TensorFlow and Keras tutorial will help you understand Deep Learning frameworks, what is TensorFlow, TensorFlow features and applications, how TensorFlow works, TensorFlow 1.0 vs TensorFlow 2.0, TensorFlow architecture with a demo. Then we will move into understanding what is Keras, models offered in Keras, what are neural networks and they work.
#tensorflow #keras #deep-learning #developer