In this article, we will write a Go language program to print the numbers from 1 to 100 concurrently with each number printed by a separate go routine. Concurrency means execution of multiple tasks parallelly and independently and making sure all the resources of the system are utilized. It can be achieved via go routines which are light weight threads and the channels. Syntax func make ([] type, size, capacity) The make function in go language is used to create an array/map it accepts the type of variable to be created, its size and capacity as arguments. Algorithm ... Read More

In this Go language article, we will write programs to take in a slice of integers and compute their sum using concurrency. Concurrency helps multiple tasks or operations to be performed simultaneously. It helps make efficient use of system resources. It is achieved using Go routines which are light-weight threads and channels which helps in the communication between the go routines. Syntax func make ([] type, size, capacity) The make function in go language is used to create an array/map it accepts the type of variable to be created, its size and capacity as arguments. func range(variable) The ... Read More

In this article, we will write Go language programs to sort a slice of integers using merge sort with concurrency. It is a process which makes the parts of a program run independently and parallelly enhancing the efficiency of the program. Go routines and channels are used to execute concurrency. Merge sort is a divide and conquer algorithm used to sort the unsorted array or slice by dividing the input slice into smaller sub-slices, individually sort them recursively and then merge them into a single slice which is sorted. Syntax func make ([] type, size, capacity) The make function ... Read More

In this article, we will write Go language programs to compute the factorial of a number using concurrency. It is a task of implementing multiple operations simultaneously and can be implemented using Go routines and channels. Go routines are the lightweight threads and channels help in non-conflicting communications between the routines. Syntax func make ([] type, size, capacity) The make function in go language is used to create an array/map it accepts the type of variable to be created, its size and capacity as arguments. Algorithm This program imports the necessary packages main and fmt. In this ... Read More

In this Go language article, we will write programs to compute all prime numbers up to a given number using concurrent execution. Concurrent execution is the process of executing multiple tasks simultaneously. In Golang, go routines and channels are used to create concurrent programs. Go routines are lightweight threads which are managed by the Go runtime and channels help in the communication between Go routines without any conflicts. Syntax func make ([] type, size, capacity) The make function in go language is used to create an array/map it accepts the type of variable to be created, its size ... Read More

In this article, we will write Go language programs to return the nth number in Fibonacci sequence using dynamic programming. It is a technique used to solve complex problems by breaking them into smaller sub problems. Memoization is the process of storing the output of the function calls in some data structure by which the next time call is made it need not calculate the output again, it can use that value to make the computation which in return lessens the execution time. Syntax func make ([] type, size, capacity) The make function in go language is used to ... Read More

Introduction Machine learning, at its core, involves teaching computers to learn patterns and make decisions without explicit programming. It has revolutionized several industries by powering intelligent systems capable of solving complex problems. One crucial aspect of machine learning that often goes unnoticed is the concept of learning curves. These curves reflect a fundamental journey where models refine their predictive abilities over time. In this article, we will explore the intriguing world of learning curves in machine learning with creative examples and detailed explanations. Learning Curves in Machine Learning Learning curves are graphical representations visualizing changes in model performance as ... Read More

Introduction License Plate Recognition (LPR) frameworks have become progressively well known in different applications, counting traffic administration, parking frameworks, and law requirement. These frameworks depend on computer vision procedures to distinguish and extricate license plate information from images or video streams. In this article, we'll investigate how to actualize an essential License Plate Recognition system utilizing OpenCV, a capable computer vision library, and Tesseract OCR, a renowned optical character recognition engine. We'll dig into the vital steps, counting picture preprocessing, character segmentation, and content recognition, to realize accurate permit plate recognition. Understanding the Components of License Plate Recognition Before ... Read More

Introduction In today's fast−paced world, machine learning models developed using frameworks like Keras have transformed various industries. However, keeping track of these models and their iterations can become a challenging task for data scientists and developers alike. CodeMonitor is an innovative tool that simplifies model versioning, monitoring, and collaboration for seamless experimentation and development workflows. In this article, we will dive into how CodeMonitor effortlessly enhances the management of Keras models through a practical example. Keras Models with CodeMonitor Version Control: With each training session or modification performed on the model saved as a separate commit or pull request ... Read More

Introduction Machine learning has revolutionized various industries, empowering them with predictive analytics and intelligent decision−making. However, before a machine can learn, it needs data to train on. One crucial step in the machine learning pipeline is splitting the available data into different subsets for training, validation, and testing purposes. This article explores what exactly is meant by splitting data for machine learning models and why it's essential for model performance. Splitting Data for Machine Learning Models For most conventional machine learning tasks, this involves creating three primary subsets: training set, validation set (optional), and test set. In essence, data splitting ... Read More