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The Data Preparation Process

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In the first part of this series, you’ve implemented various data loading techniques. You’ve seen how to load images, text, CSV files, and NumPy arrays into your Keras workspace.

Now to enable the model to make a rightful usage of your data, you’d have to convert it into an understandable format which could then be interpreted by a deep learning algorithm. To do this, you’d have to preprocess your data.

Importing Keras

First import the keras library into your workspace.

from tensorflow import keras

Now you’ll how to preprocess various kinds of data. So here’s the challenge. Every section has the task and its concerned solution. Before you jump to the solution, try solving the given task; that way, you’d be able to understand the preprocessing techniques involved. …


Data is vast, so are the data loading techniques

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Keras is a Deep Learning API of TensorFlow 2.0 used for easy and fast experimentation. It is simple to understand, flexible to extend and deploy, and powerful enough to build any neural network.

With the increase in the usage of deep learning to solve real-time problems, it has become quite a necessity to lessen the time consumed to build robust machine learning algorithms, i.e., the time taken from designing an algorithm to putting it into practice to generate the desired model has to be minimal.

Keras has been designed for this very purpose. It is a high-level deep learning API running on top of TensorFlow, a machine learning/deep learning framework. It provides an easy to use, modular, and an organised interface to solve deep learning problems. It is simple to understand and is expressive. It is a flexible API that promotes innovative research in the fields of deep learning. …


What is MLOps? Understand how it is going to help you in building an end-to-end Machine Learning Pipeline

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Machine Learning (ML) has forayed into almost all principles of our lives, be it healthcare, finance or education; it’s practically everywhere! There are numerous machine learning engineers and data scientists out there who are well versed in modelling a machine learning algorithm. Nevertheless comes the challenge of deploying a machine learning model in production. Coding a machine learning algorithm is the tip of an iceberg. For a machine learning model to be deployable, configuration, automation, server infrastructure, testing, and process management have to be taken care of. In conventional software engineering, DevOps does the engineering and operations. It bridges development and operations seamlessly. …


Know how Probability strongly influences the way you understand and implement Machine Learning

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When implementing machine learning algorithms, you may have come across situations where the environment that your algorithm is in, is non-deterministic, i.e., you cannot guarantee the same output always for the same input. Similarly in the real-world, there are scenarios such as these where the behavior can vary, though the input remains the same. Uncertainty exists no matter what. As machine learning includes humongous amounts of data, multiple hyperparameters, and a complex environment, uncertainties are bound to exist. It can be in the form of missing variables, incomplete modeling, or the data being probabilistic.

Foraying into machine learning algorithms without understanding probability seems okay at first, nonetheless diving deep into complex algorithms might push you to revisit the fundamentals. These concepts can seem complex, precisely in comprehending how math influences machine learning. You might also wonder as to how an algorithm such as Naïve Bayes is completely dependent on it! …


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‘Deep’ puts forth the notion and depth that the term Deep Learning carries. It has sprung up in the 1940s and has remained here ever since. Its success is related to its closeness to the functioning of the “human brain”.

Humans’ have always been the suave and craziest social animals on Earth. Unlike others, they are capable of thinking and decision making in the smartest way possible. Deep Learning revolves around human brain imitation and intuition, and it henceforth, gained a lot of momentum.

Understanding the core concepts is quite essential to get a hang of what Deep Learning encompasses and teaches. …


Caching Data and Queuing Jobs using Redis in Django

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1. Caching Data — An Alternative to Django Session

The session framework in Django isn’t preferable when there’s a humongous amount of data to be fetched, loaded, and set on a per-site-visitor basis. The error message, “The request’s session was deleted before the request completed. The user may have logged out in a concurrent request, for example.” could pop up when there’s an enormous amount of session data to be loaded. As session data gets loaded for every request, storing huge data can slow down your application. A Redis cache, in such cases, could act as the doppelgänger! Without any further ado, let’s quickly check how that’s done.

Step 1: Installing redis and django-redis library

yum install redis
pip3 install…


A neat UI alongside a lucid back-end to build a perfect authentication system

Let’s not make a mountain out of a molehill in understanding and coding authentication in django. Authentication, as we all know, is a basic necessity required for the users’ to pass through, before they encounter the actual treasure or the application in general. It’s a protective gear to be worn by an application to ensure secure access to it. Here, I’m going to walk you through 5 simple steps that shall help you in integrating authentication with your django application.

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To start a django project -

django-admin startproject auth_django .if windows, 
django-admin.exe startproject auth_django .

Step 1: Handling settings in settings.py

Assuming we’ve a project set up, let’s quickly define a few authentication URLs in the settings.py file in the project (auth_django) folder (an app isn’t being considered due to the simplicity of the application). …


The Human Brain Intuition and Modelling

1.0 Introduction

Computational neuroscience is the only field that can help you understand, how you’re able to think and process information in your brain. Even by the time you finished this sentence, there will be a good number of actions happening inside your brain which can be decoded by the study of neurons. The ultimate goal of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. It explains the biophysical mechanisms of computation in neurons, computer simulations of neural circuits, and models of learning.

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Before you start reading this article, there will be a few perceptions that your brain(you) might be considering, why do we need to read this? What can we learn from this? Or will this content let me know, how the brains react and solve problems in various situations? Yes! It’s all the math, permutations, chemical equations happening inside our very own brains. This article is divided into three parts. In the first part of the article, we introduce computational neuroscience in brief which include, the role that neurons play, the anatomy of the neurons and the models that could be explained for the functionalities of the brain, so called, the brain models. …


Ever imagined you dancing like MJ? “Maybe in my dreams!”, Might be your answer, but it’s indeed possible now, let’s learn to dance using Generative Adversarial Networks(GANs). GANs have gained a lot of momentum in the present technical research field, it has modified the way we think and is trying to implement the impossible.

Few astonishing things that we could do using GANS:

  • A GAN has the power to convert a boring black and white image to a colourful image.
  • It can augment the dataset using virtually generated data, and one astonishing application is, it can also synthesise images from text and vice-versa. …


We encounter several chat applications everyday, it takes lot of effort to maintain and develop these as they involve two way communication between users and logging each and every request provided by the server. In this, you’ll learn to develop a chat application using Flask, a Python web framework in just 7 minutes.

Yes, you’ve heard it right. It just takes 7 minutes of your time and blimey! you could develop a chat application and yes, have fun with it. Prerequisites for this are few concepts bound with Flask, SocketIO and jquery. …

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