Deep Learning Run Llama3.1-8B (LLM) on DigitalOcean CPU droplet In this guide at OpenGenus.org, we present the steps to setup the minimum required DigitalOcean CPU droplet, download Llama3.1-8B-Instruct model, prepare the script to run it on a sample input and generate the output. The computation time can vary from 2 to 10 minutes (or beyond).
books C++ Coding Cheatsheet: GOAT edition The book "C++: Coding Cheatsheet: GOAT edition" is the only book you need to master C++ programming concepts. The focus is on practical programming skills, design concepts and performance engineering ideas.
Multi-Agent Cyber-Physical Systems So far we've been only talking about single-agent systems. However, as we attempt to model complex systems in the real world, we shall soon find that in most cases a single agent will not suffice.
Go Programming Language Introduction to Concurrency in Go A goroutine is a function that runs concurrently to other functions. Channels provide a medium for goroutines to communicate with each other.
PyTorch Understanding the differences and use Cases of torch.Tensor.max and torch.max in PyTorch torch.Tensor.max is an instance method that applies directly to a torch.Tensor object and is mainly used to find maximum values within a single tensor. torch.max is a module-level function used to compare two tensors element-wise.
An Introduction to Cyber-Physical Systems Put in simple terms, a cyber-physical system (CPS), as the name suggests, is a dynamical system that exhibits both discrete (cyber) and continuous (physical) dynamic behavior.
Linux Understanding Linux Namespaces: The Building Blocks of Containerization Containerization uses specific features of the Linux kernel to create isolated environments for running applications.
cyber security Goals of Network Security Network security is a critical aspect of modern computer networks, ensuring the protection of data, systems, and resources from unauthorized access, damage, or disruption.
Java Basics of OOP in Java: Contact Book Program In this OpenGenus article, you will learn how to apply the basic concepts of object-oriented programming (OOP) to develop a simple, console-based contact book application.
Exploring Randomness and Statistical Simulations in Online Games The ever-changing online gaming world is built with randomness as an integral part. Randomness is needed because it gives variety and fun purely from the unpredictability of the fate of the characters' abilities.
Python Dodgeball game in PyGame [with source code] In this OpenGenus article, let us create a Dodgeball game in Python. You can customize your game along the way without having to stick to my design choices.
Machine Learning (ML) Active Learning: How Machines Learn Better with Human Guidance Active learning solves this problem by letting the model query a human expert for labels on the most uncertain or informative data points.
Deep Learning Retrieval Augmented Generation (Concepts) The RAG concept was first introduced in the paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" by Patrick Lewis and team at Facebook AI Research, which caught the attention of AI developers and industrial scientists.
Deep Learning Bilinear Upsampling In this OpenGenus article, let us discuss Bilinear Upsampling which pops up quite bit in regards to image manipulation and processing in a greater detail.
Hashing Chameleon Hash: Balancing Security and Flexibility in Cryptographic Hashing In this OpenGenus article, we examine the key properties of Chameleon hash functions, their construction, and their potential applications, and provides an overview of their advantages and limitations in modern cryptographic systems.
Computer Architecture Physical and Architectural Constraints in CPUs and GPUs Central processing units (CPUs) and graphics processing units (GPUs) are fundamental components of modern computer systems, each fulfilling different but complementary roles.
Software Engineering Unleashing the Power of .td Files: TableGen In this OpenGenus article, we cover some of the basic concepts of .td files and the functionalities and components of TableGen and its importance in the LLVM project. We will also review several use cases and benefits of using TableGen for code generation and optimization.
Software Engineering A Beginner's Guide to Kubernetes In this OpenGenus article, we have explored the topic Kubernetes from scratch with real life example.
Software Engineering Light as a Feather: The Art of the Flyweight Pattern In this OpenGenus article, we'll unravel the nuts and bolts of the Flyweight Pattern, exploring how it reduces memory footprint while optimizing performance.
Deep Learning Kolmogorov-Arnold Networks Kolmogorov-Arnold Networks (KANs) have emerged as a promising advancement in the field of neural networks, offering enhanced interpretability, efficiency, and adaptability compared to traditional architectures like the Multi-Layer Perceptron (MLPs).
Deep Learning Implementing Simple CNN model in PyTorch In this OpenGenus article, we will learn about implementing a simple CNN model using PyTorch Deep Learning framework.
PyTorch Internal Implementation of Tensors in PyTorch In this OpenGenusarticle, we'll delve into the internals of how tensors are implemented in PyTorch, referencing the code from various components of the PyTorch source code repository.
Memory Density Limits in Computers: Theoretical Boundaries and Practical Advances Memory density refers to the capacity of information that can be contained within a defined physical dimension in a memory system. The increasing demand for superior memory density in computational settings is driven by the rapid expansion of data produced across various fields.
Deep Learning as_strided op in PyTorch So you've now finally begun working towards building your first network in PyTorch. You're working with tensors in the dataset, you wish to alter their shape, or do some form of operations. The as_strided() function in PyTorch can be very useful for this.
Machine Learning (ML) Stepwise Regression We will be learning about stepwise regression- a technique that will help us find the best set of variables to choose for our linear regression.
