Oodles AI

Oodles AI


Deploying TensorFlow and Keras for Deep Learning Models

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

  1. Image Recognition and Deep Video Analytics
    Business applications of image recognition systems are entering new disrupting such as marketing, advertising, and branding along with security management. The most recent advancements of facial and object recognition systems include-

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

  1. Text Recognition
    Digitization is still in its embryonic stage. Digital transformation of businesses is yet to replace partnership deeds, client agreements, business contracts, and other essential documents. It is critical for businesses to validate the authenticity of these documents with unbiased and precise machine learning models.

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

  1. Speech Recognition
    Artificial intelligence for speech recognition models has significantly evolved from IBM’s ShoeBox to Apple’s Siri and Amazon’s Alexa. AI’s underlying natural language processing capabilities enable businesses to virtually engage their users with chatbots, shopping assistants, and conversational interfaces.

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

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Deploying TensorFlow and Keras for Deep Learning Models
Michael  Hamill

Michael Hamill


Workshop Alert! Deep Learning Model Deployment & Management

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.

#hands on deep learning #machine learning model deployment #machine learning models #model deployment #model deployment workshop

Marget D

Marget D


Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

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.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Keras Tutorial - Ultimate Guide to Deep Learning - DataFlair

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 Tutorial

Introduction to Keras

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.

Characteristics of Keras

Keras has the following characteristics:

  • It is simple to use and consistent. Since we describe models in python, it is easy to code, compact, and easy to debug.
  • Keras is based on minimal substructure, it tries to minimize the user actions for common use cases.
  • Keras allows us to use multiple backends, provides GPU support on CUDA, and allows us to train models on multiple GPUs.
  • It offers a consistent API that provides necessary feedback when an error occurs.
  • Using Keras, you can customize the functionalities of your code up to a great extent. Even small customization makes a big change because these functionalities are deeply integrated with the low-level backend.

Benefits of using Keras

The following major benefits of using Keras over other deep learning frameworks are:

  • The simple API structure of Keras is designed for both new developers and experts.
  • The Keras interface is very user friendly and is pretty optimized for general use cases.
  • In Keras, you can write custom blocks to extend it.
  • Keras is the second most popular deep learning framework after TensorFlow.
  • Tensorflow also provides Keras implementation using its tf.keras module. You can access all the functionalities of Keras in TensorFlow using tf.keras.

Keras Installation

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.

Starting with Keras

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.

Keras Sequential model

Keras Functional API

It allows you to define more complex models.

#keras tutorials #introduction to keras #keras models #keras tutorial #layers in keras #why learn keras

Mikel  Okuneva

Mikel Okuneva


Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

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:

Also Read: Why Deep Learning DevCon Comes At The Right Time

Adversarial Robustness in Deep Learning

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.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

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.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

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.

#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Gunjan  Khaitan

Gunjan Khaitan


TensorFlow And Keras Tutorial | Deep Learning With TensorFlow & Keras | Deep Learning

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