Aditya Chatterjee's Blog, page 88

Different Hyperparameter optimization techniques

In this article, we will explore various techniques used for optimizing hyperparameters of the machine learning model such as Grid Search, Bayesian Optimization, Halving randomized search and much more.

Table of contentsIntroductionManual searchGrid SearchRandomized searchBayesian OptimizationHalving grid searchHalving randomized searchChoosing the best model

Introduction

A machine learning model has two parameters: Hyperparameters and model parameters. Hyperparameters are the paramet...

Fake Coin Problem

We will see what fake coin problem is and will also see an efficient method to solve the problem.

Table of contents:

What is Fake Coin Problem?Brute force methodDecrease and Conquer TechniqueTime and Space ComplexityWhat is Fake Coin Problem?

Fake coin problem is an interesting problem in which we have to find a fake coin out of a number of coins, which is assumed to be lighter than the real coins using just a balance scale, which can be used to compare the weights of two piles of coins.

T...

How to use GraphQL with Gatsby.js

Gatsby.js is a full-stack front-end framework that is based on React and GraphQL. Gatsby.js comes natively packaged with GraphQL, alongside some additions to it. GraphQL might be suitable for your workflow.

GraphQL is a specification for querying data. Gatsby always uses GraphQL when querying data. On top of that, Gatsby can directly call GraphQL APIs.

Table of contentsWhy Gatsby.js uses GraphQLGraphiQLHow GraphQL queries work the overviewWays to use the queries with your Gatsby siteThe ga...

Divide and Conquer

In this article, we will discuss what is Divide and Conquer technique and how is it helpful. We will see various examples which uses the Divide and Conquer approach in their algorithms.

What we'll see,

Divide and ConquerQuick sort algorithmStrassen's Matrix multiplicationKaratsuba Algorithm for fast multiplicationConclusionReferenceDivide And Conquer

Divide and Conquer technique can be divided into three parts:-

Divide: The big initial problem is divided into smaller instances as sub-pr...

Mappings in Hibernate

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This article explains and shows examples of mapping of relations between entity classes in application to relationships between database tables, including standard to SQL databases one-to-many, many-to-one, one-to-one, and many-to-many relationships in Hibernate.

Table of contents:

Preparing classes for mappingXML vs AnnotationsMapping with XMLMapping with @AnnotationsAssociation MappingsMany-to-OneOne-to-ManyBidirectional association One-to-Many - Many-to-OneOne-to-OneMany-To-Many...

Spectral Clustering

Traditional clustering algorithms perform well on convex datasets but fail to correctly recognise patterns in non-convex datasets, to overcome this issue we employ Spectral clustering. Spectral clustering is an interesting Unsupervised clustering algorithm that is capable of correctly clustering Non-convex data by the use of clever Linear algebra.

Table of contents:

Non-Convex DataData GenerationK-Means clusteringSpectral clusteringOutputs & InferencePros, Cons and ConclusionNon-Convex d...

Conditional Generative Adversarial Nets

In this article, we have explained the concept of Conditional Generative Adversarial Nets in depth.

Table of contents:

GANs or Generative Adversarial NetworksArchitecture of a Generative Adversarial NetworkConditional GANsImage Labeling Using Multi-modal Learning

Pre-requisites:

Beginner's Guide to Generative Adversarial NetworksApplications of GANsTransformer Networks: How They Can Replace GANsGANs or Generative Adversarial Networks

GANs, or Generative Adversarial Networks, are a gene...

What is Neural Network and Deep Learning?

Deep Learning has become quite a buzzword in recent years. It has taken over in all applications from tasks like image recognition, chatbots like Alexa and Google Assistant to defeating world champions in a complex games like Go and Dota 2. So what exactly is deep learning and neural networks all about?

Let's first see where exactly deep learning lies and then let's explore the core of deep learning that comprises of Artificial Neural Networks.

So from the above figure , It's pretty cleared tha...

Binary Heap

Binary heap is a complete Binary tree structured as a heap data structure. Binary heaps are common way of implementing priority queues.

Binary heaps are also commonly employed in the heapsort sorting algorithm, which is an in-place algorithm because binary heaps can be implemented as an implicit data structure, storing keys in an array and using their relative positions within that array to represent child-parent relationships.

AlgorithmAverageWorst CaseSpaceO(n)O(n)SearchO(n)O(...

Basics of Hibernate (architecture + example)

Hibernate is a Java framework that maps Java classes and types into the relational databases, which solves problems of "paradigm mismatches" between how data is represented in application objects versus relational databases. This article explains the basics of Hibernate and provides a simple example with the MySQL database.

Table of Contents

OverviewArchitecture of HibernateMapping datatypesExample of Hibernate 6 with MySQL databaseInstallationConfigurationAnnotated classesDatabase tabl...