machinelearning

Convolutional Neural Network Architectures for Small Datasets

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Deep learning’s most celebrated successes—ImageNet classification, object detection, semantic segmentation—share a common ingredient: massive datasets with millions of labeled examples. ResNet trained on 1.2 million images. BERT consumed billions of words. Yet most real-world computer vision problems don’t come with millions of labeled images. Medical imaging datasets might have hundreds of scans. Manufacturing defect detection … Read more

Graph-Based ML Algorithms vs Graph Neural Networks

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Graphs are everywhere in our data-driven world. Social networks connect billions of users, molecules are represented as atoms connected by bonds, transportation systems link cities through roads and railways, and knowledge graphs organize information through relationships. When it comes to extracting insights from these graph-structured datasets, practitioners have two fundamentally different approaches at their disposal: … Read more

Online Learning Algorithms for Streaming Data: Adapting in Real-Time

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In an era where data flows continuously from countless sources—social media feeds, financial markets, IoT sensors, user interactions, and network traffic—the traditional batch learning paradigm struggles to keep pace. Batch learning assumes you can collect all your data, train a model once (or periodically retrain), and deploy it until the next training cycle. But what … Read more

Large Margin Classifiers Beyond SVMs

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Support Vector Machines (SVMs) have long been synonymous with large margin classification. Their elegant mathematical foundation and proven effectiveness made them the go-to choice for practitioners seeking classifiers that maximize the separation between classes. Yet the concept of large margin learning extends far beyond SVMs, encompassing a rich family of algorithms that apply margin-based principles … Read more

Facebook Prophet vs Classical ARIMA vs LSTM

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Time series forecasting remains one of the most practical and widely deployed machine learning applications. From predicting stock prices and sales volumes to forecasting energy consumption and website traffic, the ability to anticipate future values based on historical patterns drives critical business decisions. Yet choosing the right forecasting method can feel overwhelming—should you use the … Read more

How Decision Trees Choose Split Points Using Gini Impurity vs Entropy

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Decision trees stand as one of the most intuitive and widely-used machine learning algorithms, making complex decisions through a series of simple yes-or-no questions. At the heart of every decision tree lies a critical challenge: how to determine the best way to split data at each node. This seemingly simple question has profound implications for … Read more

Custom Model Deployment with SageMaker Endpoints

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Deploying machine learning models to production is one of the most critical yet challenging phases of any ML project. While training a model that achieves excellent accuracy on test data is an accomplishment, the real value emerges only when that model serves predictions reliably at scale. Amazon SageMaker Endpoints provide a powerful managed infrastructure for … Read more

Building Low-Latency Inference APIs Using FastAPI and ONNX

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Latency kills user experience and revenue. In production ML systems, every millisecond of inference delay compounds across millions of requests—a model taking 200ms instead of 50ms doesn’t just slow down four requests, it reduces your system’s throughput capacity by 75% and degrades user experience enough to measurably impact conversion rates. Whether you’re serving recommendations that … Read more

Best Practices for Deploying ML Models with Docker + FastAPI in Production

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Deploying machine learning models to production environments represents the critical bridge between data science experimentation and real-world business value. While Jupyter notebooks and research codebases excel at model development, they fall catastrophically short when serving predictions at scale with reliability, security, and performance requirements that production systems demand. The gap between a trained model achieving … Read more

ML Models for User Retention Prediction in Mobile Apps

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User retention represents the lifeblood of mobile app success. While acquiring new users through marketing campaigns captures headlines and investment, retaining those users determines long-term viability and profitability. The harsh reality of mobile apps is brutal: industry averages show that 75% of users abandon apps within the first week, and 90% churn within the first … Read more