How to use Naive Bayes Theorem in Machine Learning
Suppose you are a product manager, you want to classify customer reviews in positive and…
How to use Naive Bayes Theorem in Machine Learning
Suppose you are a product manager, you want to classify customer reviews in positive and negative classes. Or As a loan manager, you want to identify which loan applicants are safe or risky? As a healthcare analyst, you want to predict which patients can suffer from diabetes disease. All the examples have the same kind…
Clustering in Machine Learning
In real world, not every data we work upon has a target variable. This kind of data cannot be analyzed using supervised learning algorithms. We need the help of unsupervised algorithms. One of the most popular type of analysis under unsupervised learning is Cluster analysis. When the goal is to group similar data points in a…
A Guide to Optimizing Neural Networks for Large-Scale Deployment
Deep Learning has revolutionized various industries, leading to the continuous development of new business applications. However, as AI models are growing in complexity, they require significantly more memory and increasingly powerful computing systems to function effectively. Global AI market is projected to reach USD 1.85 trillion by 2030. As AI moves towards edge devices — which often…
Detecting and Mitigating Data Anomaly in ML
Data anomalies in Machine Learning (ML) can pose significant challenges to the accuracy and reliability of models. Detecting and mitigating these anomalies are crucial processes for managing ML models and their applications. Data anomalies can arise from a variety of factors that are essential for data scientists and knowledge workers to handle: Human errors Feature inaccuracy Dataset…
Learn How to Do Model Fine-Tuning: Challenges, Metrics & Best Practices
Fine-tuning models have gained momentum with the advent of transfer learning, particularly in natural language processing (NLP). Instead of starting from scratch, practitioners tweak sophisticated models initially developed by tech giants like Google or Microsoft.
huawei reveals mate XT, the world’s first tri-fold smartphone
Mate XT Ultimate Design, a tri-fold smartphone from Huawei Hours after Apple unveiled its iPhone 16, Huawei surprises its fans with the release of the Mate XT Ultimate Design, dubbed the world’s first tri-fold AI smartphone with three large screens made of glass. This is also the debut of Huawei’s Tiangong hinge system, which allows the smartphone to bend both its…
Apple iPhone 16 Pro Max – Full phone specifications
After months of rumors, the iPhone 16 has finally arrived. And though Apple may once again have showered its iPhone 16 Pro models with more upgrades, the entry-level flagship still can boast enough enhancements to attract would-be buyers — especially anyone intrigued by the prospect of Apple Intelligence features coming later this year. The iPhone 16 features design changes in…
A Crash Course on Distributed Systems
A distributed system is a collection of computers, also known as nodes, that collaborate to perform a specific task or provide a service. These nodes are physically separate and communicate with each other by passing messages over a network. Distributed systems can span geographical boundaries, enabling them to utilize resources from different locations. …
Personal Data Classification
Airbnb is built on trust. One key way we maintain trust with our community is by ensuring that personal data is handled with care, in a manner that meets security, privacy, and compliance requirements. Understanding where and what personal data exists is foundational to this. Over the past several years, we’ve built our own data…
A Deep Dive into Airbnb’s Server-Driven UI System
Background: Server-Driven UI Before we dive into Airbnb’s implementation of server-driven UI (SDUI), it’s important to understand the general idea of SDUI and how it provides an advantage over traditional client-driven UI. In a traditional world, data is driven by the backend and the UI is driven by each client (web, iOS, and Android). As…
