Author: Shubham Goyal

MachineX: Medical Image Analyses for Malaria Detection

Reading Time: 9 minutes In this blog we will see the implementation of a neural network which will help us to detect malaria in a blood sample. In our previous blog MachineX: Malaria detection using Artificial Intelligence , we had talked about why Ai is important to make it more accurate and how. Now before we begin, I’d like to point out that I am neither a doctor nor Continue Reading

MachineX: Malaria detection using Artificial Intelligence

Reading Time: 5 minutes In this blog we will talk about why Malaria detection is important to detect early presence of parasitized cells in a thin blood smear. Introduction Malaria is a deadly, infectious mosquito-borne disease caused by Plasmodium parasites. These parasites are transmitted by the bites of infected female Anopheles mosquitoes. While we won’t get into details about the disease, there are five main types of malaria. Let’s Continue Reading

MachineX: Alphabets of PyTorch (Part 1)

Reading Time: 6 minutes Overview In this blog, you’ll get an introduction to deep learning using the PyTorch framework, we will see some basics of PyTorch. Introduction to PyTorch PyTorch is a Python machine learning package based on Torch, which is an open-source machine learning package based on the programming language Lua. Two main features: Tensor computation (like NumPy) with strong GPU acceleration Automatic differentiation for building and training Continue Reading

MachineX: Welcome to TensorFlow 2.0

Reading Time: 3 minutes With the release of Tensorflow 2.0 we have taken another step towards machine dominating human dysphoria. Just kidding!!, this debate is for big people like Elon Musk, Mark Juckerberg or Jack Ma. We are just happy that Tensorflow 2.0 has been released and it will make our life a lot easier as 2.0 is being improved with consideration of freedbacks from its users. As part Continue Reading

MachineX: Image Data Augmentation Using Keras

Reading Time: 4 minutes In this blog , we will focus on Image Data Augmentation using keras and how we can implement same. Problem When we work with image classification projects, the input which a user will give can vary in many aspects like angles, zoom and stability while clicking the picture. So we should train our model to accept and make sense of almost all types of inputs. Continue Reading

MachineX: Generative Adversary Networks (GAN)

Reading Time: 6 minutes In this blog , we are going to talk about GAN(Generative Adversary Networks) Basics and how they actually works. GAN is about creating, like drawing a portrait or composing a symphony. This is hard compared to other deep learning fields. It is much easier to identify a Monet painting than painting one, by computers or by people. But it brings us closer in understanding intelligence. Continue Reading

MachineX: k-Nearest Neighbors(KNN) for Regression

Reading Time: 5 minutes Introduction K Nearest Neighbor Regression (KNN) works in much the same way as KNN for classification. The difference lies in the characteristics of the dependent variable. With classification KNN the dependent variable is categorical. With regression KNN the dependent variable is continuous. Both involve the use neighboring examples to predict the class or value of other examples. In this blog we will understand the basics Continue Reading

Machine X: Text Summarization in Python

Reading Time: 5 minutes In this blog, we will learn about the different type of text summarization methods and at the end, we will see a practical of the same. We all interact with applications that use text summarization. several of these applications are for the platform that publishes articles on daily news, amusement, sports. With our busy schedule, we have a tendency to choose to read the summary of this article before we decide to jump in for reading the whole article. Reading a summary help us to spot the Continue Reading

MachineX :k-Nearest Neighbors(KNN) for classification

Reading Time: 4 minutes In this blog, we are going to go through about one of the widely used classification algorithm called KNN (K-Nearest Neighbors). Since I started doing data science, I observed that most of the problems end up with classification model The main reason behind this biased property is, most of the analytic problems are based on decision making. For instance, to identify loan applicants as low, Continue Reading

MachineX: The alphabets of Artificial Neural Network – (Part 2)

Reading Time: 3 minutes If you are reading this blog, it is supposed that you have already done with Part 1 No???? Then visit to the previous blog The alphabets of Artificial Neural Network first and comeback here for an awesome knowledge about Neural network working. We got the basic understanding of neural network so let’s get into deep. Let’s understand how neural networks work. Once you got the Continue Reading

MachineX: The alphabets of Artificial Neural Network

Reading Time: 4 minutes In this blog, we will talk about Neural network which is the base of deep learning which gave machine learning and ultra edge in the current AI revolution. Let’s get started!!!!!! before diving into deep learning, let’s know – Why Deep Learning ??? Well, there are plenty of reason , few of them are: Deep learning is most popular than shallow level learning once you Continue Reading

Lagom Zero Hour: CQRS Concepts

Reading Time: 4 minutes In this blog, we will discuss CQRS and how it is different from old approaches. CQRS stands for Command Query Responsibility Segregation. The approach that individuals use for interacting with a data system is to treat it as a CRUD datastore. By this, I mean that we have a mental model of some record structure. We think about producing new records, scanning records, updating existing records, Continue Reading

Scala Zero Hour: Lists

Reading Time: 6 minutes A class for immutable linked lists representing ordered collections of elements of type. This class comes with two implementing case classes scala.Nil and scala.:: that implement the abstract members isEmpty, head and tail. This class is optimal for,last-in-first-out (LIFO) stack-like access patterns. Given below are a few examples val myList = List(3, 2, 1) myList: List[Int] = List(1, 2, 3, 4)val myListwith4 = 4 :: myList // Continue Reading

Knoldus Pune Careers - Hiring Freshers

Get a head start on your career at Knoldus. Join us!