Gussie  Hansen

Gussie Hansen


Real-time Age, Gender and Emotion Prediction from Webcam with Keras and OpenCV

In the era of Covid-19, we become more reliant on virtual interactions such as Zoom meetings / Teams chat. These livestream webcam videos have become a rich data source to explore. This article will explore the use case of age, gender and emotion prediction which could facilitate sales person to understand their customers better, for example.

#deep-learning #machine-learning #data-science #computer-vision

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Real-time Age, Gender and Emotion Prediction from Webcam with Keras and OpenCV

Eran Feit



This is a nice and fun Python tutorial that enables to de-age a face and basically transform a face into target age.

This is effect is based on Python and Style-based library.

The outcome is impressive.

You can find the link for the video tutorial here:

You can find in the video description an instructions file with the setup process, reference for the Github library




Ian  Robinson

Ian Robinson


4 Real-Time Data Analytics Predictions for 2021

Data management, analytics, data science, and real-time systems will converge this year enabling new automated and self-learning solutions for real-time business operations.

The global pandemic of 2020 has upended social behaviors and business operations. Working from home is the new normal for many, and technology has accelerated and opened new lines of business. Retail and travel have been hit hard, and tech-savvy companies are reinventing e-commerce and in-store channels to survive and thrive. In biotech, pharma, and healthcare, analytics command centers have become the center of operations, much like network operation centers in transport and logistics during pre-COVID times.

While data management and analytics have been critical to strategy and growth over the last decade, COVID-19 has propelled these functions into the center of business operations. Data science and analytics have become a focal point for business leaders to make critical decisions like how to adapt business in this new order of supply and demand and forecast what lies ahead.

In the next year, I anticipate a convergence of data, analytics, integration, and DevOps to create an environment for rapid development of AI-infused applications to address business challenges and opportunities. We will see a proliferation of API-led microservices developer environments for real-time data integration, and the emergence of data hubs as a bridge between at-rest and in-motion data assets, and event-enabled analytics with deeper collaboration between data scientists, DevOps, and ModelOps developers. From this, an ML engineer persona will emerge.

#analytics #artificial intelligence technologies #big data #big data analysis tools #from our experts #machine learning #real-time decisions #real-time analytics #real-time data #real-time data analytics

Hello Jay

Hello Jay


Keras vs. OpenCV - Differences Between Keras and OpenCV

OpenCV is the open-source library for computer vision and image processing tasks in machine learning. OpenCV provides a huge suite of algorithms and aims at real-time computer vision. Keras, on the other hand, is a deep learning framework to enable fast experimentation with deep learning. In this Keras Tutorial, we will learn about Keras Vs OpenCV.

Keras Vs OpenCV

First, we will see both the technologies, their application, and then the differences between keras and OpenCv.

About OpenCV

Computer Vision is defined for understanding meaningful descriptions of physical objects from the image.

OpenCV was built to provide an infrastructure for computer vision. This library has a huge range of optimized machine learning and computer vision algorithms. These algorithms include object identification, detecting and recognizing faces, object movement tracking, etc. OpenCV provides support for C++, Python, Java and MATLAB programming languages and works on Windows, Linux, Android and Mac Operating Systems.

The common features in OpenCV are read and write images, save and capture images/videos, filter or transform the image, detecting faces,eyes,cars in images or videos, perform feature detection, background subtraction, and tracking objects.

Applications of OpenCV

  • In Robotics, OpenCV is useful in domains like navigation, obstacle avoiding, and in human-robot interaction.
  • In the medical industry it is useful for classification and detection of diseases, for analyzing brain MRI scans and in surgeries.
  • For security purposes, like in biometric scan and video surveillance.
  • In transportation and autonomous vehicles, self-driving cars.

#keras tutorials #keras vs opencv #keras #opencv

Jessica Smith

Jessica Smith



Build a Real Time chat application that can integrated into your social handles. Add more life to your website or support portal with a real time chat solutions for mobile apps that shows online presence indicators, typing status, timestamp, multimedia sharing and much more. Users can also log into the live chat app using their social media logins sparing them from the need to remember usernames and passwords. For more information call us at +18444455767 or email us at or Visit:

#real time chat solutions for mobile apps #real time chat app development solutions #live chat software for mobile #live chat software solutions #real time chat app development #real time chat applications in java script

Philian Mateo

Philian Mateo


Compile, Evaluate and Predict Model in Keras

Welcome to DataFlair Keras Tutorial series. This chapter explains how to compile, evaluate and make predictions from Model in Keras.

Keras Compile Models

After defining our model and stacking the layers, we have to configure our model. We do this configuration process in the compilation phase.

Before training the model we need to compile it and define the loss function, optimizers, and metrics for prediction.

We compile the model using .compile() method.

model.compile ( optimizer, loss, metrics, loss_weights, sample_weight_mode, weighted_metrics, target_tensors)

Optimizer, loss, and metrics are the necessary arguments.

Keras provides various loss functions, optimizers, and metrics for the compilation phase.

#keras evaluate #keras predict #model in keras #keras

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