Author: Pranjut Gogoi

MachineX: AI in Manufacturing controlling Quality

Reading Time: 3 minutes Manufacturing is a very simple business. Here the owner buys the raw material or parts to manufacture a finished product. However, it’s a risky business in terms of selling the finished products. Supply too much and you flood the market, causing a drop in price and a drop in profits. By not meeting demand, the customer may go elsewhere with a drop in sales for Continue Reading

MachineX: Starts With Why ft. Convolutional Neural Network

Reading Time: 4 minutes If you are looking for a short answer, I would say real life image dataset are not small like MNIST to build a model with a fully connected neural network. But let’s produce some dopamine and explore the convolutional neural network a bit more in-depth. Our Visual Cortex focuses on certain areas to identify any image and similarly Convolutional Neural Network also focuses on the Continue Reading

MachineX: An Introduction to KSAI, a machine learning library

Reading Time: 3 minutes Take a closer look at Linkedin or any media platform for a couple of minutes, you’ll find that the hot topic in the technology section nowadays is Machine Learning and Artificial Intelligence. Why Machine learning and artificial intelligence? Well needless to say it is transforming the world like anything. People are doing good in business by predicting different aspects, doctors are doing good in medical Continue Reading

We do continous retro, do you?

Reading Time: 3 minutes If you are reading this blog right now you are already following scrum or trying to follow Scrum. Well, scrum has a lot of good practices which can surely make your project a success. Following it right is what most people get stuck on. One of its ceremonies is called retrospective which is quite important, inevitable in fact, but still many don’t do it. Leaving Continue Reading

MachineX: Choosing Support Vector Machine over other classifiers

Reading Time: 4 minutes When one has to select the best classifier from all the good options, often he would be on the horns of a dilemma. Decision tree/Random Forest, ANN, KNN, Logistic regression etc. are some options that often used for the choice of classification. Every one of it has its pros and cons and when to select the best one the probably the most important thing to Continue Reading

MachineX: The second dimensionality reduction method

Reading Time: 5 minutes In the previous blog we have gone through how more data or to be precise more dimensions in the data creates different problems like overfitting in classification and regression algorithms. This is known as “curse of dimensionality”. Then we have gone through the solutions to the problem i.e. dimensionality reduction. We were mainly focused on one of the dimensionality reduction method called feature selection. In this Continue Reading

MachineX: When data is a curse to learning

Reading Time: 4 minutes Data and learning are like best friends, perhaps learning is too dependent on data to be called as friends. When data overwhelms, learning acts pricey, so it feels more like a girlfriend-boyfriend sort of a relationship. Well don’t get confused or bothered on how I am comparing the data and learning, it is just my depiction of something called Dimensionality reduction in machine learning. On Continue Reading

The combat in Knoldus

Reading Time: 3 minutes Virtue is a state of war, and to live in it we have always to combat with ourselves. – Jean-Jacques Rousseau Knoldus held a Hackathon like competition within the organization on 11th June 2017. It was simply named “code-combat”. Knoldus is an organization which always encourages every individual to push his or her boundary and get the excellence out of him that could be achieved Continue Reading

Learning the maths for Feed Forward Network

Reading Time: 4 minutes In my last blog we were discussing about the biological motivation on the artificial neural network. This time we will discuss more about the artificial neural network in practice. In ANN we have different layers of networks to solve a problem. For which problem, how many layers required to solve it, is a different topic and will be writing a blog on that soon, however Continue Reading

When akka stream meets RabbitMQ

Reading Time: 3 minutes “Reactive Streams is an initiative to provide a standard for asynchronous stream processing with non-blocking back pressure.”  – that is how reactive streams are defined in wikipedia. There are two other implementation to reactive streams then Akka-streams i.e. reactor and Netflix’s RxJava. However, since the reactive stream manifesto published the only mature implementation available is akka-stream. According to reactive manifesto the implementation must follow the following Continue Reading

First interaction Artificial Neural Network

Reading Time: 4 minutes I hated biology in my school days and loved mathematics. After a long period of time I get to learn something which combines both mathematics and biology together, that is Artificial Neural Network short for ANN, inspired by biological Neural network. Though you might find it weird, that is how I would like to define the artificial neural network. When we say biology here, it Continue Reading

(Code Dissection) Akka Quartz Scheduler Scala’s way of scheduling(Part -2)

Reading Time: 5 minutes I hope you guys are doing good, and had a fresh breath. Put your mask again if you find the previous topic smelly, as we are going to finish up the dissection for Akka Quartz Scheduler. I am going to refer the every Quartz-Scheduler things prefixed with java. So when I say Java-Quartz-Scheduler, I mean the the quartz library which is made through java, and Continue Reading

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