Aditya Chatterjee's Blog, page 15

Cold start and Data sparsity problem in Recommender system

Index:InitializationThe Importance of Recommender SystemsAn overview of the Cold Start Problem
3.1 The Cold Start Problem in Other DL Systems
3.2 Issues Arising from the Cold Start Problem
3.3 Technical Solutions and ImpactThe Data Sparsity Problem
4.1 Data Sparsity in Other DL Systems
4.3 Issues Arising from Data Sparsity
4.3 Technical Solutions and ImpactDifferentiating Between Cold Start and Data SparsityPractical ApplicationsConclusionReferences1. Introduction:

Recommender systems...

Canary and Rolling Deployments strategies explained

What is a Deployment Strategy?

Nothing beats the sensation of pushing your code to production.
The feeling you get upon seeing your code going live, to be tried out by real users, is just mesmerizing. But, this feeling can be short-lived or may even remain unexperienced if the deployment strategy employed isn't the most suitable one for the project in question or a given scenario.

According to OpenShift:

A deployment strategy is a way to change or upgrade an application with the aim of no perce...

Minimize Maximum Distance between Gas Stations [Solved 3 approaches]

KEY TAKEAWAYSEnhance practical problem-solving skills through gas station optimization strategies.Master Binary Search adaptation for decimal-answer problems.Conduct comparative analysis of algorithmic approaches.Develop step-by-step problem-solving techniques.Achieve efficient precision in optimization problems.Table of Contents:Problem Statement OverViewUnderstanding the ProblemApproach 1: Brute ForceApproach 2: Heap-Based SolutionApproach 3: Binary SearchComparative Analysis o...

Streamlit library in Python [Practical Introduction]

Key TakeawaysStreamlit is a Python library that enables us to quickly build front-end web pages for our data application by using just Python codes.Streamlit allows us to draw various widgets including different charts, tables, images and input elements like form, button, slider etc.After developing our application, we can easily deploy and share it by using GitHub and StreamLit Community Cloud, which is a free service.

Data analysis is one of the areas where Python shines as a language. ...

Web Video Text Tracks Format (WebVTT): for adding Video Subtitles

TABLE OF CONTENTS

What is WebVTT?WebVTT filesWebVTT bodyWebVTT cuesWebVTT commentsStyling WebVTT cuesInstance methods and propertiesSpecificationsBrowser compatibilityWebVTT TutorialKey Takeaways (WebVTT)

WebVTT is a format for HTML5 video captions, subtitles and overlays. It is a text file with timed cues, and it starts with a WebVTT body. Cues display text at specific times, while comments are added as notes. You can style cues to enhance presentation.

What is WebVTT?

WebVTT (W...

JvmStatic, JvmOverloads and JvmFields Annotations in Kotlin

Key Takeaways (JvmOverloads, JvmField, and JvmStatic) `@JvmOverloads`, `@JvmField`, and `@JvmStatic` are annotations in kotlin to improve interoperability with the Java code. `@JvmStatic` is used to mark a companion object function or property as a static method in the generated Java bytecode. `@JvmOverloads` is used to generate overloads for the kotlin functions with default values in Java. `@JvmField` is used to mark a kotlin attribute as public fields in Java code.

In...

Concept and Data Drift in Deep Learning

Key TakeawaysModels assume constant patterns, but real-world data changes; monitor for concept drift (shift in relationships) for accurate predictions.Data characteristics evolve over time; be vigilant for data drift (distributional changes) to maintain model relevance.Utilize monitoring tools to detect shifts in data patterns, ensuring models adapt to changing environments.Regularly update models with new data to counter concept and data drift, preserving prediction accuracy. Continuous...

Polyak Averaging

Key TakeawaysPolyak averaging, also known as Polyak-Ruppert averaging, is a technique used in machine learning to improve the performance and stability of models, especially in the context of deep learning.Polyak Averaging smoothens training by averaging model parameters, reducing sensitivity to noisy updates.Averaging parameters improves the model's ability to generalize, leading to better performance on unseen data.In reinforcement learning, it balances exploring new actions and exploit...

System Design of an Incident Response Platform

Table of contents

IntroductionIncident Response PlatformFunctionalityHigh-level designSelect monitoring targetsDetect incidentLook into incidentClassify incidentRespond to incidentContaining incidentEliminating incidentRecover from incidentDatabase solutionCapacity estimatesStorageNumber of servers neededConcepts and technologiesNetwork segmentationIntrusion Detection SystemsWeb Application FirewallsIndicators of compromiseData backup softwareNetwork-attached...

Neural Scaling Law: A Brief Introduction

Table of ContentsIntroductionWhat is a neural network model?What is a neural scaling law?What are the types of neural scaling laws?Specific examples of neural scaling lawsReal-world applications of neural scaling lawsKey TakeawaysNeural Scaling Law predicts the relation between a property of a Deep Learning model to an attribute to number of parameters, size of training dataset and others.There are different variants of the law.One example is Jeff Dean's scaling law which states ac...