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    <title>blog.ayuslh.in</title>
    <link>https://blog.ayuslh.in</link>
    <description>Ayush Arora's learning notes and write-ups.</description>
    
    <item>
      <title><![CDATA[Ch.39: Decision Tree Hyperparameters]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch39-decision-tree-hyperparameters</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch39-decision-tree-hyperparameters</guid>
      <description><![CDATA[How max_depth, min_samples_split, min_samples_leaf, criterion, splitter, max_features, max_leaf_nodes, and min_impurity_decrease each pull an unconstrained decision tree back from overfitting, with real sklearn decision boundaries showing the underfit-to-overfit trade-off for each one.]]></description>
      <pubDate>Sun, 30 Aug 2026 22:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.38: Entropy, Information Gain, and Gini Impurity]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch38-entropy-information-gain-gini</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch38-entropy-information-gain-gini</guid>
      <description><![CDATA[The missing piece from the last post: how a decision tree actually decides which feature to split on. Deriving entropy as a measure of disorder, computing it by hand on the Play Tennis dataset, using information gain to pick the best split, meeting Gini impurity as CART's faster alternative, and extending both to numeric features via threshold search.]]></description>
      <pubDate>Sun, 30 Aug 2026 21:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.37: Decision Trees, the Core Intuition]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch37-decision-tree-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch37-decision-tree-intuition</guid>
      <description><![CDATA[How a decision tree is really just a nested series of if/else questions asked on the data, why it flips the usual root-at-bottom tree image upside down, what happens once features are numeric instead of categorical, and the terminology and trade-offs that come with the idea.]]></description>
      <pubDate>Sun, 30 Aug 2026 21:00:00 GMT</pubDate>
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    <item>
      <title><![CDATA[Ch.36: The Kernel Trick, In Code]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch36-kernel-trick-in-code</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch36-kernel-trick-in-code</guid>
      <description><![CDATA[sklearn's SVC turns the geometric lift from Ch.35 into one keyword argument. A linear SVM fails on concentric circles, manually engineering z = x1² + x2² fixes it, and kernel="rbf" gets the same result without ever building that feature by hand, which is the actual trick in kernel trick.]]></description>
      <pubDate>Sun, 30 Aug 2026 10:10:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.35: The Kernel Trick, Geometrically]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch35-kernel-trick-geometric-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch35-kernel-trick-geometric-intuition</guid>
      <description><![CDATA[SVM's margin-maximizing line only works if a line can separate the classes at all. The kernel trick sidesteps that limit by lifting data into a higher dimension where it becomes linearly separable, illustrated with two concrete examples: a 1D red-green-red line lifted to 2D by squaring, and 2D concentric circles lifted to 3D by an RBF-style transform.]]></description>
      <pubDate>Sun, 30 Aug 2026 09:35:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.34: Soft Margin SVM and the Hinge Loss]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch34-soft-margin-svm-and-hinge-loss</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch34-soft-margin-svm-and-hinge-loss</guid>
      <description><![CDATA[Hard margin SVM breaks the moment data isn't perfectly separable. Soft margin SVM fixes this with slack variables, one per point, that measure how badly a constraint is violated, folded into the objective through a C-weighted penalty. Rewriting that penalty as a per-point loss produces the hinge loss, SVM's answer to logistic regression's log loss.]]></description>
      <pubDate>Sun, 30 Aug 2026 08:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.33: The Math Behind Hard Margin SVM]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch33-hard-margin-svm-math</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch33-hard-margin-svm-math</guid>
      <description><![CDATA[Turning SVM's margin intuition into an actual optimization problem: the +1/-1 supporting hyperplanes, the yi(w.xi+b) >= 1 constraint that unifies both classes into one rule, the distance-formula derivation of margin width 2/||w||, and why maximizing that margin becomes minimizing (1/2)||w||^2.]]></description>
      <pubDate>Sun, 30 Aug 2026 07:57:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.32: Support Vector Machines, the Maximum Margin Intuition]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch32-support-vector-machines-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch32-support-vector-machines-intuition</guid>
      <description><![CDATA[Introducing SVM: why two lines that both separate the training data perfectly aren't equally good, what 'margin' means geometrically, why the closest points (the support vectors) are the only ones that matter, and where SVM extends past a straight line.]]></description>
      <pubDate>Sat, 29 Aug 2026 21:16:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.31: K-Nearest Neighbors, Intuition and Failure Cases]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch31-k-nearest-neighbors-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch31-k-nearest-neighbors-intuition</guid>
      <description><![CDATA[Introducing KNN: classifying a point by majority vote among its K closest training points, why it needs no real training phase, how the decision surface visualizes what a classifier actually learned, and the six situations where KNN quietly falls apart.]]></description>
      <pubDate>Sat, 29 Aug 2026 19:06:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.30: Logistic Regression's Hyperparameters]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch30-logistic-regression-hyperparameters</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch30-logistic-regression-hyperparameters</guid>
      <description><![CDATA[A practical tour of scikit-learn's LogisticRegression knobs, penalty, C, solver, max_iter, multi_class, class_weight, and how each one maps back to a concept already covered in this series.]]></description>
      <pubDate>Sat, 29 Aug 2026 12:57:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.29: Fitting Non-Linear Data With Polynomial Logistic Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch29-polynomial-logistic-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch29-polynomial-logistic-regression</guid>
      <description><![CDATA[Logistic Regression always draws a straight decision boundary, which fails on non-linearly separable data. Borrowing the same trick Polynomial Regression used for curves, PolynomialFeatures lets Logistic Regression fit curved boundaries too, at the cost of a new degree hyperparameter to tune.]]></description>
      <pubDate>Sat, 29 Aug 2026 12:44:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.28: Softmax Regression for Multi-Class Classification]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch28-softmax-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch28-softmax-regression</guid>
      <description><![CDATA[Extending Logistic Regression past two classes: the softmax function, why training one binary model per class is the intuitive but slow approach, and how a single modified loss function trains one joint multinomial model instead.]]></description>
      <pubDate>Sat, 29 Aug 2026 12:37:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.27: The ROC Curve and AUC]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch27-roc-curve-auc</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch27-roc-curve-auc</guid>
      <description><![CDATA[Why classifiers output probabilities, not labels; how the classification threshold trades false positives against false negatives; and how the ROC curve and AUC evaluate a model across every possible threshold at once instead of just one.]]></description>
      <pubDate>Fri, 28 Aug 2026 19:46:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.26: Precision, Recall, and F1 Score]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch26-precision-recall-f1-score</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch26-precision-recall-f1-score</guid>
      <description><![CDATA[Fixing accuracy's blind spot by scoring the two error types separately: precision for when false positives are the dangerous mistake, recall for when false negatives are, F1 as their harmonic mean, and how both generalize to multi-class problems via macro and weighted averages.]]></description>
      <pubDate>Fri, 28 Aug 2026 17:58:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Database Replication]]></title>
      <link>https://blog.ayuslh.in/system-design-foundation/database-replication</link>
      <guid>https://blog.ayuslh.in/system-design-foundation/database-replication</guid>
      <description><![CDATA[Splitting the database into a write master and multiple read replicas: why it improves performance, reliability, and availability, and what happens when a master or a slave goes offline.]]></description>
      <pubDate>Thu, 27 Aug 2026 21:39:17 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Load Balancer]]></title>
      <link>https://blog.ayuslh.in/system-design-foundation/load-balancer</link>
      <guid>https://blog.ayuslh.in/system-design-foundation/load-balancer</guid>
      <description><![CDATA[Putting a load balancer in front of the web tier: how traffic flows through it, why web servers move to private IPs, and how it gives the web tier failover and easy scaling.]]></description>
      <pubDate>Thu, 27 Aug 2026 21:14:44 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Vertical vs Horizontal Scaling]]></title>
      <link>https://blog.ayuslh.in/system-design-foundation/vertical-vs-horizontal-scaling</link>
      <guid>https://blog.ayuslh.in/system-design-foundation/vertical-vs-horizontal-scaling</guid>
      <description><![CDATA[Two ways to give a system more capacity: a bigger server (scale up) or more servers (scale out), why scale up runs out of room, and why that leads to a load balancer.]]></description>
      <pubDate>Thu, 27 Aug 2026 21:07:36 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.25: Accuracy, the Confusion Matrix, and Why Accuracy Lies]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch25-accuracy-confusion-matrix</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch25-accuracy-confusion-matrix</guid>
      <description><![CDATA[Defining accuracy for classification, why there's no universal 'good enough' threshold, and why the confusion matrix (TP, FP, FN, TN) exists to fix accuracy's biggest blind spot on imbalanced datasets, illustrated with a terrorist-detection example.]]></description>
      <pubDate>Thu, 27 Aug 2026 11:58:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.24: Logistic Regression's Gradient Descent, Derived and Coded]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch24-logistic-regression-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch24-logistic-regression-gradient-descent</guid>
      <description><![CDATA[Rewriting log loss in matrix form, differentiating it with respect to the weight vector, and coding the resulting gradient descent update rule into a from-scratch Logistic Regression that matches scikit-learn's.]]></description>
      <pubDate>Thu, 27 Aug 2026 11:35:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.23: Deriving Logistic Regression's Loss Function]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch23-logistic-regression-loss-function</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch23-logistic-regression-loss-function</guid>
      <description><![CDATA[Using maximum likelihood to turn 'which of two lines is better' into an actual number: multiplying per-point probabilities, fixing the vanishing-product problem with logs, and arriving at binary cross-entropy.]]></description>
      <pubDate>Thu, 27 Aug 2026 10:53:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Database]]></title>
      <link>https://blog.ayuslh.in/system-design-foundation/database</link>
      <guid>https://blog.ayuslh.in/system-design-foundation/database</guid>
      <description><![CDATA[Splitting the web tier and data tier onto separate servers, and choosing between relational (SQL) and non-relational (NoSQL) databases.]]></description>
      <pubDate>Thu, 27 Aug 2026 09:24:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.22: Swapping the Step Function for Sigmoid]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch22-step-function-to-sigmoid</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch22-step-function-to-sigmoid</guid>
      <description><![CDATA[Replacing the perceptron trick's step function with sigmoid so correctly classified points keep pushing the line, closing most of the margin gap with real Logistic Regression, and setting up why one problem still remains.]]></description>
      <pubDate>Wed, 26 Aug 2026 21:06:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.21: Coding the Perceptron Trick (And Why It's Not Enough)]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch21-perceptron-trick-from-scratch</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch21-perceptron-trick-from-scratch</guid>
      <description><![CDATA[Implementing the Perceptron Trick from scratch in numpy, then comparing its decision boundary against Scikit-Learn's actual LogisticRegression to expose the real limitation: the perceptron stops the moment every point is classified correctly, while Logistic Regression keeps improving toward a better-margin line.]]></description>
      <pubDate>Wed, 26 Aug 2026 11:37:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.20: Logistic Regression: The Perceptron Trick]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch20-logistic-regression-perceptron-trick</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch20-logistic-regression-perceptron-trick</guid>
      <description><![CDATA[Building the geometric intuition behind Logistic Regression through the Perceptron Trick: representing a decision boundary as a weight vector, and deriving a single unified update rule that nudges a misclassified line toward the point it got wrong.]]></description>
      <pubDate>Wed, 26 Aug 2026 10:10:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.19: Elastic Net Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch19-elastic-net-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch19-elastic-net-regression</guid>
      <description><![CDATA[Elastic Net combines Ridge's L2 penalty and Lasso's L1 penalty into one loss function, controlled by two hyperparameters (or Scikit-Learn's alpha and l1_ratio), for datasets where it isn't obvious in advance whether every feature matters or only a few do.]]></description>
      <pubDate>Wed, 26 Aug 2026 09:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.18: Why Lasso Zeroes Out Coefficients (And Ridge Never Does)]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch18-why-lasso-zeroes-coefficients</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch18-why-lasso-zeroes-coefficients</guid>
      <description><![CDATA[Deriving the single-feature closed-form solution for Lasso Regression and showing exactly why lambda subtracts from the numerator instead of adding to the denominator, the algebraic reason Lasso coefficients can hit exactly zero and Ridge coefficients never do.]]></description>
      <pubDate>Wed, 26 Aug 2026 09:38:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.17: Lasso Regression: The Intuition]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch17-lasso-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch17-lasso-regression</guid>
      <description><![CDATA[Introducing Lasso (L1) Regression alongside Ridge: how the L1 penalty drives coefficients to exactly zero, performs automatic feature selection, and reshapes the loss function into a V-shaped kink that Ridge's smooth parabola never has.]]></description>
      <pubDate>Wed, 26 Aug 2026 09:21:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.16: Five Things to Know About Ridge Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch16-ridge-regression-key-understandings</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch16-ridge-regression-key-understandings</guid>
      <description><![CDATA[Five interview-style intuitions about Ridge Regression: how coefficients shrink with alpha, why larger coefficients shrink faster, the bias-variance tradeoff, how alpha reshapes the loss surface, and why it's called Ridge.]]></description>
      <pubDate>Wed, 26 Aug 2026 08:42:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.15: Ridge Regression via Gradient Descent]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch15-ridge-regression-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch15-ridge-regression-gradient-descent</guid>
      <description><![CDATA[Deriving Ridge's gradient descent update from the same matrix loss used for the closed-form solution, then implementing it from scratch to match Scikit-Learn's Ridge and SGDRegressor on the diabetes dataset.]]></description>
      <pubDate>Wed, 26 Aug 2026 08:13:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.14: Ridge Regression: The Math and the Code]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch14-ridge-regression-code</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch14-ridge-regression-code</guid>
      <description><![CDATA[Deriving the closed-form solution to Ridge Regression, first for a single feature and then in full matrix form for n dimensions, and implementing both from scratch to match Scikit-Learn's Ridge class coefficient for coefficient.]]></description>
      <pubDate>Tue, 25 Aug 2026 23:09:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Single Server Setup]]></title>
      <link>https://blog.ayuslh.in/system-design-foundation/single-server-setup</link>
      <guid>https://blog.ayuslh.in/system-design-foundation/single-server-setup</guid>
      <description><![CDATA[Starting from the simplest possible architecture: everything, web app, database, cache, running on one server, and tracing the request flow from DNS to response.]]></description>
      <pubDate>Tue, 25 Aug 2026 19:19:15 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.13: Ridge Regression: Regularization and the L2 Penalty]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch13-ridge-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch13-ridge-regression</guid>
      <description><![CDATA[Understanding regularization in linear models: bagging, boosting, and regularization as the three tools against overfitting, why high coefficient values signal overfitting, and how the L2 penalty term reshapes the loss function to fix it.]]></description>
      <pubDate>Tue, 25 Aug 2026 08:41:29 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.12: Bias-Variance Tradeoff]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch12-bias-variance-tradeoff</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch12-bias-variance-tradeoff</guid>
      <description><![CDATA[What bias and variance actually mean, using three models fit to the same data: one too simple, one just right, one that memorizes noise, and why the goal is always low bias and low variance together.]]></description>
      <pubDate>Mon, 17 Aug 2026 20:40:28 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.11: Polynomial Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch11-polynomial-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch11-polynomial-regression</guid>
      <description><![CDATA[Why plain linear regression fails on curved datasets, how feature transformation turns non-linear problems into linear ones with PolynomialFeatures, navigating the degree trade-off, and extending to multiple features.]]></description>
      <pubDate>Mon, 17 Aug 2026 20:38:24 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.10: Mini-Batch Gradient Descent]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch10-mini-batch-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch10-mini-batch-gradient-descent</guid>
      <description><![CDATA[The middle ground between batch GD and SGD: update on small random batches of rows instead of one row or the whole dataset, implement MBGDRegressor from scratch, and see why it converges as fast as SGD but far more smoothly.]]></description>
      <pubDate>Sun, 16 Aug 2026 14:26:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.9: Stochastic Gradient Descent]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch9-stochastic-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch9-stochastic-gradient-descent</guid>
      <description><![CDATA[Why batch GD breaks on large data, how SGD fixes it by updating parameters one random row at a time, implementing SGDRegressor from scratch, and understanding the noise-vs-speed tradeoff with a real convergence comparison.]]></description>
      <pubDate>Sun, 16 Aug 2026 14:10:28 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.8: Batch Gradient Descent for Multiple Linear Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch8-batch-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch8-batch-gradient-descent</guid>
      <description><![CDATA[Extending gradient descent from one feature to many: deriving the vectorized coefficient update rule, implementing GDRegressor from scratch, and benchmarking it against sklearn's OLS on the diabetes dataset.]]></description>
      <pubDate>Sun, 16 Aug 2026 13:34:28 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.7: Gradient Descent - Intuition, Math, and Implementation]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch7-gradient-descent</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch7-gradient-descent</guid>
      <description><![CDATA[Deriving batch gradient descent for linear regression, building a custom GDRegressor class from scratch, animating live convergence, and comparing converged parameters against OLS.]]></description>
      <pubDate>Sun, 16 Aug 2026 10:24:46 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.31: Why HTTP/2 Is a Binary Protocol]]></title>
      <link>https://blog.ayuslh.in/networking/ch31-http-2-binary-protocol</link>
      <guid>https://blog.ayuslh.in/networking/ch31-http-2-binary-protocol</guid>
      <description><![CDATA[A deep dive into why HTTP/2 is called a binary protocol: contrasting HTTP/1.1's text-based line-by-line string traversal and split-by-delimiter parsing with HTTP/2's fixed 9-byte binary frame header that is parsed by reading raw bits at predefined offsets.]]></description>
      <pubDate>Sat, 15 Aug 2026 20:30:00 GMT</pubDate>
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    <item>
      <title><![CDATA[PostgreSQL Connection Pooling: Architecture, Bottlenecks, and PgBouncer Deep Dive]]></title>
      <link>https://blog.ayuslh.in/til/postgres-connection-pooling</link>
      <guid>https://blog.ayuslh.in/til/postgres-connection-pooling</guid>
      <description><![CDATA[A deep dive into PostgreSQL's process-per-connection model, the max_connections trap, RAM/CPU context switching bottlenecks, back-pressure pooling, and using PgBouncer for scalable database performance.]]></description>
      <pubDate>Sat, 15 Aug 2026 11:40:00 GMT</pubDate>
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    <item>
      <title><![CDATA[Ch.30: HTTP/2 Frames in Depth]]></title>
      <link>https://blog.ayuslh.in/networking/ch30-http-2-frames</link>
      <guid>https://blog.ayuslh.in/networking/ch30-http-2-frames</guid>
      <description><![CDATA[An in-depth look at HTTP/2 frame architecture: RFC 9113 frame header format, HEADERS vs DATA frames, END_HEADERS and END_STREAM flags, and Wireshark traces of GET and POST requests.]]></description>
      <pubDate>Sat, 15 Aug 2026 07:50:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.29: HTTP/2 and Multiplexing]]></title>
      <link>https://blog.ayuslh.in/networking/ch29-http-2-multiplexing</link>
      <guid>https://blog.ayuslh.in/networking/ch29-http-2-multiplexing</guid>
      <description><![CDATA[How HTTP/2 solves HTTP/1.1's head-of-line blocking with multiplexing and Stream IDs: multiple requests fly over a single TCP connection simultaneously, responses can arrive in any order, and a numeric Stream ID on every frame ties each response back to its request without relying on arrival order.]]></description>
      <pubDate>Fri, 14 Aug 2026 20:10:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.6: Multiple Linear Regression - The Math]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch6-multiple-linear-regression-math</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch6-multiple-linear-regression-math</guid>
      <description><![CDATA[Deriving the closed-form normal equation for multiple linear regression in matrix form, coding a custom estimator from scratch, and validating it against sklearn on the diabetes dataset.]]></description>
      <pubDate>Fri, 14 Aug 2026 15:00:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.5: Multiple Linear Regression - The Intuition]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch5-multiple-linear-regression-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch5-multiple-linear-regression-intuition</guid>
      <description><![CDATA[Extending simple linear regression to more than one input column: fitting a plane through 3D data instead of a line, and what the hyperplane equation looks like in n dimensions.]]></description>
      <pubDate>Fri, 14 Aug 2026 09:50:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.4: Assumptions of Linear Regression]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch4-assumptions-of-linear-regression</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch4-assumptions-of-linear-regression</guid>
      <description><![CDATA[The five core assumptions of ordinary least squares linear regression, why each one matters, what happens when it is violated, and how to verify them using Python.]]></description>
      <pubDate>Fri, 14 Aug 2026 06:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.3: Regression Metrics - MAE, MSE, RMSE, R² and Adjusted R²]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch3-regression-metrics</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch3-regression-metrics</guid>
      <description><![CDATA[How to tell whether a regression line is any good: the five standard metrics, what each one measures geometrically and mathematically, why each one exists, and where each one falls short.]]></description>
      <pubDate>Fri, 14 Aug 2026 03:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.2: Simple Linear Regression - The Math]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch2-simple-linear-regression-math</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch2-simple-linear-regression-math</guid>
      <description><![CDATA[Deriving the closed-form OLS solution for simple linear regression by hand, coding a linear regression class from scratch, and comparing it to sklearn on the same placement dataset.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.1: Simple Linear Regression - The Intuition]]></title>
      <link>https://blog.ayuslh.in/machine-learning-algorithms/ch1-simple-linear-regression-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning-algorithms/ch1-simple-linear-regression-intuition</guid>
      <description><![CDATA[Kicking off a new series on core ML algorithms with the first one everyone learns: linear regression, the placement dataset it's usually taught on, and what slope and intercept actually mean.]]></description>
      <pubDate>Thu, 13 Aug 2026 07:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.40: PCA in Practice - MNIST, Visualization, and Where It Breaks Down]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch40-pca-mnist-and-limitations</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch40-pca-mnist-and-limitations</guid>
      <description><![CDATA[Applying sklearn's PCA to the real MNIST digit dataset for both dimensionality reduction and 2D visualization, then closing out the series with the three shapes of data where PCA simply doesn't help.]]></description>
      <pubDate>Wed, 12 Aug 2026 21:20:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.39: Principal Component Analysis, Part 2 - The Math Behind It]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch39-principal-component-analysis-math</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch39-principal-component-analysis-math</guid>
      <description><![CDATA[Turning PCA's geometric intuition into a real optimization problem: projections, the variance objective, covariance matrices, and why eigenvectors of the covariance matrix are exactly the axes PCA is looking for, ending with a full code walkthrough on real 3D data.]]></description>
      <pubDate>Wed, 12 Aug 2026 19:15:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.38: Principal Component Analysis, Part 1 - Geometric Intuition and Variance]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch38-principal-component-analysis-intuition</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch38-principal-component-analysis-intuition</guid>
      <description><![CDATA[Building intuition for PCA from the ground up: why feature selection breaks down on correlated columns, the geometric idea of rotating axes to find new ones, and why variance is the exact quantity PCA maximizes, illustrated with real generated plots.]]></description>
      <pubDate>Wed, 12 Aug 2026 17:05:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.37: The Curse of Dimensionality, Feature Selection, and Feature Extraction]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch37-feature-extraction-and-feature-selection</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch37-feature-extraction-and-feature-selection</guid>
      <description><![CDATA[Why adding more features to a model can hurt rather than help: the Curse of Dimensionality explained from first principles, the optimal-feature-count plateau, how sparse high-dimensional space breaks distance-based algorithms, and the two solution paths (Feature Selection vs Feature Extraction) that make up the final pillars of feature engineering.]]></description>
      <pubDate>Wed, 12 Aug 2026 14:11:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.36: Feature Construction and Feature Splitting]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch36-feature-construction-and-feature-splitting</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch36-feature-construction-and-feature-splitting</guid>
      <description><![CDATA[How to manually engineer new features from existing ones (Feature Construction) and how to break apart columns that pack multiple pieces of information into a single cell (Feature Splitting), demonstrated on the Titanic dataset with real accuracy numbers.]]></description>
      <pubDate>Tue, 11 Aug 2026 20:50:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.35: Percentile Method for Outlier Detection and Removal]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch35-percentile-method-for-outlier-detection</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch35-percentile-method-for-outlier-detection</guid>
      <description><![CDATA[A complete guide to detecting and handling outliers using the Percentile Method (Winsorization): understanding percentile thresholds, computing rank-based boundaries without any distributional assumption, and applying Trimming and Capping on a real height dataset.]]></description>
      <pubDate>Tue, 11 Aug 2026 18:25:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.34: IQR Method for Outlier Detection and Removal]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch34-iqr-method-for-outlier-detection</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch34-iqr-method-for-outlier-detection</guid>
      <description><![CDATA[A complete guide to detecting and handling outliers in skewed distributions using the IQR Proximity Rule: understanding box plots and quartiles, computing Q1/Q3/IQR boundaries, and applying Trimming and Capping on a real placement dataset.]]></description>
      <pubDate>Tue, 11 Aug 2026 16:10:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.33: Z-Score Method for Outlier Detection and Removal]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch33-z-score-method-for-outlier-detection</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch33-z-score-method-for-outlier-detection</guid>
      <description><![CDATA[A complete step-by-step guide to detecting and handling outliers using the Z-score and 3-sigma rule: mathematical assumptions, distribution check on a real placement dataset, Z-score standard transformation formulas, and practical Python implementations of both Trimming and Capping (Winsorization).]]></description>
      <pubDate>Tue, 11 Aug 2026 10:49:36 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.32: Introduction to Outliers in Machine Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch32-introduction-to-outliers</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch32-introduction-to-outliers</guid>
      <description><![CDATA[A comprehensive foundational guide to outliers in feature engineering: what they are, why they distort weight-based machine learning models, when to remove vs. keep them, which algorithms are sensitive vs. robust, four main treatment strategies (trimming, capping, missing value imputation, discretization), and three primary detection frameworks (Z-score, IQR rule, percentile thresholds).]]></description>
      <pubDate>Tue, 11 Aug 2026 08:18:29 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.31: Iterative Imputer (MICE)]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch31-iterative-imputer-mice</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch31-iterative-imputer-mice</guid>
      <description><![CDATA[A hand-worked walkthrough of Multivariate Imputation by Chained Equations (MICE): why it assumes data is Missing at Random, how it treats every missing column as a regression target predicted from the rest, why the fill values fluctuate before settling down, a real convergence run on the 50 Startups dataset, and a scikit-learn IterativeImputer comparison against mean and KNN imputation on Titanic.]]></description>
      <pubDate>Mon, 10 Aug 2026 19:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.28: Structure of an HTTP Request and Response]]></title>
      <link>https://blog.ayuslh.in/networking/ch28-structure-of-http-request-and-response</link>
      <guid>https://blog.ayuslh.in/networking/ch28-structure-of-http-request-and-response</guid>
      <description><![CDATA[A field-by-field walkthrough of what an actual HTTP request and response look like: the request line (method, path, version), common request headers like Host, Referer, and Cookie, the response status line and headers like Content-Length and Server, and how ETag plus If-None-Match lets a server answer with a bodyless 304 Not Modified instead of resending the same JSON.]]></description>
      <pubDate>Mon, 10 Aug 2026 17:51:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.30: KNN Imputer and Multivariate Imputation]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch30-knn-imputer</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch30-knn-imputer</guid>
      <description><![CDATA[A beginner-friendly walkthrough of the KNN Imputer, scikit-learn's first multivariate imputation technique: how nan-aware Euclidean distance finds the nearest rows despite missing values, how uniform vs. distance-weighted averaging fills the gap, a hand-worked example on real Titanic rows, a comparison against mean imputation on accuracy and on how well each preserves correlation between columns, and the memory/speed cost that comes with it.]]></description>
      <pubDate>Mon, 10 Aug 2026 14:15:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.27: HTTP/1.0 vs. HTTP/1.1]]></title>
      <link>https://blog.ayuslh.in/networking/ch27-http-1.0-vs-http-1.1</link>
      <guid>https://blog.ayuslh.in/networking/ch27-http-1.0-vs-http-1.1</guid>
      <description><![CDATA[Why HTTP has multiple versions, and what each one fixed. HTTP/0.9's lack of headers, HTTP/1.0's optional keep-alive, HTTP/1.1's persistent connections by default, and why pipelining exists but stays disabled because of head-of-line blocking.]]></description>
      <pubDate>Mon, 10 Aug 2026 07:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.29: Random Sample Imputation, Missing Indicator, and Automatic Parameter Selection]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch29-random-sample-imputation-and-missing-indicator</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch29-random-sample-imputation-and-missing-indicator</guid>
      <description><![CDATA[A beginner-friendly walkthrough of random sample imputation for numerical and categorical columns, why it preserves distribution shape and variance far better than mean/median or mode imputation, the reproducibility trap it introduces in production, scikit-learn's MissingIndicator for flagging which rows were incomplete, and using GridSearchCV to automatically pick the best imputation strategy instead of guessing.]]></description>
      <pubDate>Mon, 10 Aug 2026 03:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.26: HTTP, the HyperText Transfer Protocol]]></title>
      <link>https://blog.ayuslh.in/networking/ch26-http-hypertext-transfer-protocol</link>
      <guid>https://blog.ayuslh.in/networking/ch26-http-hypertext-transfer-protocol</guid>
      <description><![CDATA[What HTTP actually is, broken down from its own name: hypertext, transfer, and protocol. Why it's an application-layer protocol built on top of TCP, why it's stateless, why it's media-independent, and what it deliberately leaves to other protocols like TLS.]]></description>
      <pubDate>Sun, 09 Aug 2026 07:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.25: Anatomy of a DNS Message]]></title>
      <link>https://blog.ayuslh.in/networking/ch25-anatomy-of-a-dns-message</link>
      <guid>https://blog.ayuslh.in/networking/ch25-anatomy-of-a-dns-message</guid>
      <description><![CDATA[A field-by-field breakdown of the DNS message header: the Transaction ID, all ten flag bits (QR, Opcode, AA, TC, RD, RA, Z, AD, CD, RCODE), and the four count fields that define the Question, Answer, Authority, and Additional sections, including what glue records are and why they exist. Ends with a real Wireshark capture showing DNS falling back from UDP to TCP mid-query.]]></description>
      <pubDate>Sun, 09 Aug 2026 06:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.28: Categorical Imputation with Most Frequent Category and Missing Category]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch28-categorical-imputation</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch28-categorical-imputation</guid>
      <description><![CDATA[A beginner-friendly walkthrough of univariate imputation for categorical columns: filling missing values with the most frequent category (mode) versus creating a new 'Missing' category, when each one is appropriate, and how mode imputation can badly distort a column's category proportions when no single category dominates, demonstrated with scikit-learn's SimpleImputer on the Ames housing dataset.]]></description>
      <pubDate>Sat, 08 Aug 2026 03:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.24: DNS Record Types, A, AAAA, MX, CNAME, NS, TXT, and PTR]]></title>
      <link>https://blog.ayuslh.in/networking/ch24-dns-record-types</link>
      <guid>https://blog.ayuslh.in/networking/ch24-dns-record-types</guid>
      <description><![CDATA[What each DNS record type actually stores and why: A and AAAA for IPv4/IPv6, MX and the full SMTP delivery flow with priority failover, CNAME aliasing and its no-dead-end rule, NS records and DNS delegation across providers, TXT records and how SPF stops spoofed mail, and PTR records for reverse DNS and sender reputation.]]></description>
      <pubDate>Fri, 07 Aug 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.27: Numerical Imputation with Mean, Median, Arbitrary Value, and End of Distribution]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch27-mean-median-imputation</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch27-mean-median-imputation</guid>
      <description><![CDATA[A beginner-friendly walkthrough of univariate imputation for numerical columns: filling missing values with mean or median, an arbitrary out-of-range value, or a value at the end of the distribution, with pandas and scikit-learn's SimpleImputer plus ColumnTransformer, and a look at how each technique distorts variance, distribution shape, and correlation, demonstrated on the Titanic dataset.]]></description>
      <pubDate>Thu, 06 Aug 2026 03:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.23: DNS, the Domain Name System]]></title>
      <link>https://blog.ayuslh.in/networking/ch23-dns-domain-name-system</link>
      <guid>https://blog.ayuslh.in/networking/ch23-dns-domain-name-system</guid>
      <description><![CDATA[What DNS actually solves beyond just being unable to remember IP addresses: dynamic IPs, GeoDNS, and load balancing. The hierarchy behind a URL (root zone, TLD, domain, subdomain), how a DNS resolver walks the root, TLD, and authoritative name servers to resolve a domain, and how caching and TTL keep most lookups fast.]]></description>
      <pubDate>Wed, 05 Aug 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.26: Handling Missing Data with Complete Case Analysis]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch26-complete-case-analysis</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch26-complete-case-analysis</guid>
      <description><![CDATA[A beginner-friendly introduction to handling missing data in machine learning, starting with Complete Case Analysis (CCA): what it is, when the MCAR assumption and the 5% rule make it safe to use, and how to verify it with pandas by comparing distributions and category ratios before and after dropping rows, demonstrated on a real data science job applicant dataset.]]></description>
      <pubDate>Wed, 05 Aug 2026 03:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.22: TCP Congestion Control, Slow Start, and Congestion Avoidance]]></title>
      <link>https://blog.ayuslh.in/networking/ch22-tcp-congestion-control</link>
      <guid>https://blog.ayuslh.in/networking/ch22-tcp-congestion-control</guid>
      <description><![CDATA[The difference between flow control and congestion control, how a router's limited buffer can drop segments even when the receiver has room, TCP's congestion window and slow start threshold, the Slow Start and Congestion Avoidance algorithms, how TCP detects congestion via retransmission timeouts, triple duplicate ACKs, and ECN, and how the sender's window is always the minimum of the receiver window and congestion window.]]></description>
      <pubDate>Tue, 04 Aug 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.24: Handling Mixed Variables: When One Column Hides Two Kinds of Data]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch24-handling-mixed-variables</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch24-handling-mixed-variables</guid>
      <description><![CDATA[A beginner-friendly walkthrough of mixed variables in feature engineering: columns that pack a category and a number into the same cell (Titanic's Cabin and Ticket), or that mix rows of numbers with rows of pure category (a family-count column with an 'Alone' label), handled with pandas string methods, regex extraction, and pd.to_numeric.]]></description>
      <pubDate>Tue, 04 Aug 2026 01:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.25: Working with Date and Time Columns in Feature Engineering]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch25-working-with-dates-and-times</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch25-working-with-dates-and-times</guid>
      <description><![CDATA[A beginner-friendly walkthrough of extracting features from datetime columns in pandas: converting object columns with pd.to_datetime, pulling out year, month, day, day of week, is_weekend, week number, quarter, and semester, extracting hour/minute/second from timestamps, and computing elapsed time between two dates using timedelta and np.timedelta64, demonstrated on real NYC taxi trip data.]]></description>
      <pubDate>Mon, 03 Aug 2026 21:48:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.21: TCP Flow Control, Sliding Window, and Window Scaling Explained]]></title>
      <link>https://blog.ayuslh.in/networking/ch21-tcp-flow-control-window-size-and-window-scaling</link>
      <guid>https://blog.ayuslh.in/networking/ch21-tcp-flow-control-window-size-and-window-scaling</guid>
      <description><![CDATA[How TCP prevents a fast sender from overwhelming a slow receiver: the Stop-and-Wait problem, cumulative acknowledgments, the receive buffer and Window Size field, the sliding window technique, and how Window Scaling works around the 16-bit Window Size limit.]]></description>
      <pubDate>Sun, 02 Aug 2026 19:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.20: MTU, MSS, and Path MTU Discovery]]></title>
      <link>https://blog.ayuslh.in/networking/ch20-mtu-mss-and-path-mtu-discovery</link>
      <guid>https://blog.ayuslh.in/networking/ch20-mtu-mss-and-path-mtu-discovery</guid>
      <description><![CDATA[How large a single IP packet can actually be: the Maximum Transmission Unit (MTU), what happens when a packet exceeds it (IP fragmentation), how TCP's Maximum Segment Size (MSS) is calculated to avoid that fragmentation, and how Path MTU Discovery (PMTUD) uses the Don't Fragment flag and ICMP to find the smallest MTU along an entire network path.]]></description>
      <pubDate>Sun, 02 Aug 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.23: Discretization and Binarization: Turning Numbers into Categories]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch23-discretization-binarization</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch23-discretization-binarization</guid>
      <description><![CDATA[A beginner-friendly walkthrough of converting numerical columns into categorical ones using scikit-learn: Equal Width, Equal Frequency, and K-Means binning with KBinsDiscretizer, plus threshold-based Binarization with Binarizer.]]></description>
      <pubDate>Sun, 02 Aug 2026 03:00:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.22: Power Transformer: Letting Math Pick the Best Transform for You]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch22-power-transformer</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch22-power-transformer</guid>
      <description><![CDATA[How scikit-learn's PowerTransformer automatically searches for the best exponent to reshape a skewed column toward a normal distribution, using Box-Cox and Yeo-Johnson, and why Yeo-Johnson also handles zero and negative values that Box-Cox can't.]]></description>
      <pubDate>Sun, 02 Aug 2026 02:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.21: Function Transformer: Reshaping Skewed Data with Math]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch21-function-transformer</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch21-function-transformer</guid>
      <description><![CDATA[A beginner-friendly guide to mathematical feature transformations in scikit-learn: why some models want normally distributed data, how to check if a column is skewed, and how to fix it with Log, Reciprocal, Square, and Square Root transforms using FunctionTransformer.]]></description>
      <pubDate>Sun, 02 Aug 2026 01:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.20: Pipelines: Chaining Preprocessing and Modeling Into One Object]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch20-pipelines</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch20-pipelines</guid>
      <description><![CDATA[How scikit-learn's Pipeline chains imputation, encoding, scaling, feature selection, and a model into a single object, why the manual alternative falls apart the moment you deploy, and how to visualize, inspect, and ship a fitted pipeline.]]></description>
      <pubDate>Sat, 01 Aug 2026 02:00:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.19: ColumnTransformer: Applying Multiple Preprocessing Steps in One Shot]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch19-column-transformer</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch19-column-transformer</guid>
      <description><![CDATA[How scikit-learn's ColumnTransformer replaces the tedious manual work of imputing, encoding, and reassembling columns one at a time, with a hands-on comparison of the manual approach vs. a single ColumnTransformer call.]]></description>
      <pubDate>Fri, 31 Jul 2026 00:40:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.18: Encoding Categorical Data: One-Hot Encoding]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch18-one-hot-encoding</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch18-one-hot-encoding</guid>
      <description><![CDATA[A practical guide to One-Hot Encoding for nominal categorical data: why Ordinal and Label Encoding break down for unordered categories, the Dummy Variable Trap and multicollinearity, pandas get_dummies vs. scikit-learn's OneHotEncoder, and how to handle high-cardinality columns.]]></description>
      <pubDate>Fri, 31 Jul 2026 00:10:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.19: Anatomy of a TCP Segment]]></title>
      <link>https://blog.ayuslh.in/networking/ch19-anatomy-of-a-tcp-segment</link>
      <guid>https://blog.ayuslh.in/networking/ch19-anatomy-of-a-tcp-segment</guid>
      <description><![CDATA[A detailed breakdown of the TCP segment header down to the bit level: Source/Destination Port, Sequence/Acknowledgment Numbers, Data Offset, all 8 control flags (CWR, ECE, URG, ACK, PSH, RST, SYN, FIN), Window Size, Checksum, Urgent Pointer, and the Options field, including why the header can grow up to 60 bytes.]]></description>
      <pubDate>Thu, 30 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.18: TCP Sequence and Acknowledgment Numbers]]></title>
      <link>https://blog.ayuslh.in/networking/ch18-tcp-sequence-and-acknowledgment-numbers</link>
      <guid>https://blog.ayuslh.in/networking/ch18-tcp-sequence-and-acknowledgment-numbers</guid>
      <description><![CDATA[A detailed breakdown of TCP Sequence and Acknowledgment numbers: how bytes are numbered across segments, how receivers reorder out-of-order data, detect loss and duplicates, and how initial sequence numbers are negotiated during the 3-way handshake.]]></description>
      <pubDate>Wed, 29 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.17: Encoding Categorical Data: Ordinal Encoding and Label Encoding]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch17-ordinal-and-label-encoding</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch17-ordinal-and-label-encoding</guid>
      <description><![CDATA[A practical guide to handling categorical data in machine learning: understanding Nominal vs. Ordinal data, Ordinal Encoding for ordered input features, Label Encoding for target columns, the key difference between the two, and a hands-on walkthrough with scikit-learn.]]></description>
      <pubDate>Mon, 27 Jul 2026 23:22:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.16: Feature Scaling: Normalization (Min-Max Scaling)]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch16-feature-scaling-normalization</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch16-feature-scaling-normalization</guid>
      <description><![CDATA[A comprehensive guide to Normalization in Feature Scaling: the Min-Max Scaling formula and geometric intuition, a hands-on walkthrough on the Wine dataset, Mean Normalization, Max Absolute Scaling, Robust Scaling, and when to choose Normalization over Standardization.]]></description>
      <pubDate>Mon, 27 Jul 2026 22:45:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.13: Bivariate and Multivariate Data Analysis: Visualizing Feature Interactions]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch13-bivariate-and-multivariate-data-analysis</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch13-bivariate-and-multivariate-data-analysis</guid>
      <description><![CDATA[A comprehensive guide to Bivariate and Multivariate Data Analysis in EDA: analyzing Numerical vs Numerical, Numerical vs Categorical, and Categorical vs Categorical feature interactions using Seaborn plots, heatmaps, cross-tabulations, pair plots, and cluster maps.]]></description>
      <pubDate>Mon, 27 Jul 2026 21:48:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.15: Feature Scaling: Standardization (Z-Score Normalization)]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch15-feature-scaling-standardization</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch15-feature-scaling-standardization</guid>
      <description><![CDATA[A comprehensive guide to Feature Scaling and Standardization: understanding why feature scaling is necessary, the mathematics of Z-score normalization, mean centering, variance scaling, preventing data leakage, and algorithmic sensitivity.]]></description>
      <pubDate>Mon, 27 Jul 2026 21:39:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.14: Introduction to Feature Engineering: Concepts, Taxonomy, and ML Lifecycle]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch14-introduction-to-feature-engineering</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch14-introduction-to-feature-engineering</guid>
      <description><![CDATA[A comprehensive guide to Feature Engineering in Machine Learning: understanding its definition, role in the ML lifecycle, and exploring its 4 major pillars (Transformation, Construction, Selection, and Extraction).]]></description>
      <pubDate>Mon, 27 Jul 2026 21:18:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.17: TCP Connection Termination (4-Way Handshake)]]></title>
      <link>https://blog.ayuslh.in/networking/ch17-tcp-connection-termination-four-way-handshake</link>
      <guid>https://blog.ayuslh.in/networking/ch17-tcp-connection-termination-four-way-handshake</guid>
      <description><![CDATA[A detailed breakdown of TCP connection teardown: exploring why 4-way termination with FIN and ACK is required, half-closed connections, state machines, TIME-WAIT state, and RST reset aborts.]]></description>
      <pubDate>Sun, 26 Jul 2026 20:28:33 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.16: TCP 3-Way Handshake (SYN, SYN-ACK, ACK)]]></title>
      <link>https://blog.ayuslh.in/networking/ch16-tcp-three-way-handshake</link>
      <guid>https://blog.ayuslh.in/networking/ch16-tcp-three-way-handshake</guid>
      <description><![CDATA[A clear walkthrough of the TCP 3-way handshake: why a connection has to be established before any data is sent, the SYN, SYN-ACK, and ACK exchange step by step, and why TCP is called a stateful protocol.]]></description>
      <pubDate>Sat, 25 Jul 2026 22:41:29 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Understanding Diffie-Hellman Key Exchange: Intuition, Math, and Security]]></title>
      <link>https://blog.ayuslh.in/til/diffie-hellman-key-exchange</link>
      <guid>https://blog.ayuslh.in/til/diffie-hellman-key-exchange</guid>
      <description><![CDATA[A detailed breakdown of Diffie-Hellman Key Exchange: covering key exchange intuition, the color mixing analogy, modular arithmetic clock faces, the discrete logarithm problem, and mathematical proofs.]]></description>
      <pubDate>Fri, 24 Jul 2026 22:25:48 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.15: Transmission Control Protocol (TCP)]]></title>
      <link>https://blog.ayuslh.in/networking/ch15-transmission-control-protocol-tcp</link>
      <guid>https://blog.ayuslh.in/networking/ch15-transmission-control-protocol-tcp</guid>
      <description><![CDATA[A comprehensive overview of Transmission Control Protocol (TCP), exploring connection-oriented transport, 3-way handshakes, stateful tracking, segment fragmentation, reliability, in-order delivery, TCP vs UDP comparisons, and real-world application use cases.]]></description>
      <pubDate>Fri, 24 Jul 2026 20:51:41 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.12: Univariate Data Analysis: Exploring Categorical and Numerical Features]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch12-univariate-data-analysis</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch12-univariate-data-analysis</guid>
      <description><![CDATA[A comprehensive guide to Univariate Data Analysis in EDA: understanding data types, visualizing categorical distributions with count plots and pie charts, analyzing numerical distributions using histograms, KDE plots, box plots, 5-number summary, and skewness.]]></description>
      <pubDate>Thu, 23 Jul 2026 21:59:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.11: Asking the Right Questions to Understand Your Data]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch11-asking-basic-questions-about-data</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch11-asking-basic-questions-about-data</guid>
      <description><![CDATA[A practical guide to the initial step of data understanding: 7 fundamental questions to inspect dataset shape, bias, memory, missing values, summary statistics, duplicates, and correlation.]]></description>
      <pubDate>Thu, 23 Jul 2026 20:41:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[The Thundering Herd Problem (Cache Stampede)]]></title>
      <link>https://blog.ayuslh.in/til/system-design-thundering-herd-problem</link>
      <guid>https://blog.ayuslh.in/til/system-design-thundering-herd-problem</guid>
      <description><![CDATA[An in-depth guide to the Thundering Herd Problem (Cache Stampede), why synchronized concurrent requests overwhelm backend resources during cache misses or service recoveries, and architectural mitigation techniques like distributed locking, jitter, and request coalescing.]]></description>
      <pubDate>Wed, 22 Jul 2026 22:29:08 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.14: Anatomy of a UDP Datagram and Checksum]]></title>
      <link>https://blog.ayuslh.in/networking/ch14-anatomy-of-a-udp-datagram-and-checksum</link>
      <guid>https://blog.ayuslh.in/networking/ch14-anatomy-of-a-udp-datagram-and-checksum</guid>
      <description><![CDATA[A detailed breakdown of the UDP datagram header fields, RFC 768 specification, the math behind 16-bit checksum calculation, and how receivers detect in-flight packet corruption.]]></description>
      <pubDate>Wed, 22 Jul 2026 19:46:31 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.13: User Datagram Protocol (UDP)]]></title>
      <link>https://blog.ayuslh.in/networking/ch13-user-datagram-protocol-udp</link>
      <guid>https://blog.ayuslh.in/networking/ch13-user-datagram-protocol-udp</guid>
      <description><![CDATA[A deep dive into User Datagram Protocol (UDP), exploring connectionless transport, port addressing, UDP vs TCP performance trade-offs, DNS over UDP mechanics, TCP meltdown, and real-time use cases.]]></description>
      <pubDate>Tue, 21 Jul 2026 22:15:40 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.12: ICMP, Ping, and Traceroute]]></title>
      <link>https://blog.ayuslh.in/networking/ch12-icmp-ping-and-traceroute</link>
      <guid>https://blog.ayuslh.in/networking/ch12-icmp-ping-and-traceroute</guid>
      <description><![CDATA[A comprehensive guide to Internet Control Message Protocol (ICMP), how Ping measures RTT, how Traceroute cleverly uses TTL to discover intermediate routers, and why asterisks (***) appear in traceroute output.]]></description>
      <pubDate>Tue, 21 Jul 2026 21:16:07 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.10: Framing a Machine Learning Problem]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch10-framing-ml-problem</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch10-framing-ml-problem</guid>
      <description><![CDATA[A comprehensive guide on how to translate a business problem into a machine learning problem, using a Netflix churn rate case study.]]></description>
      <pubDate>Sun, 19 Jul 2026 22:36:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.11: Routing in Detail]]></title>
      <link>https://blog.ayuslh.in/networking/ch11-routing</link>
      <guid>https://blog.ayuslh.in/networking/ch11-routing</guid>
      <description><![CDATA[A deep dive into how routing works across different network scenarios, from local switches to internet-wide hops reaching AWS.]]></description>
      <pubDate>Sat, 18 Jul 2026 20:28:30 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.10: OSI Model vs TCP/IP Model]]></title>
      <link>https://blog.ayuslh.in/networking/ch10-osi-vs-tcp-ip-model</link>
      <guid>https://blog.ayuslh.in/networking/ch10-osi-vs-tcp-ip-model</guid>
      <description><![CDATA[Understanding the 7 layers of the OSI model, how they compare to the TCP/IP model, and how a request travels through these layers over the network.]]></description>
      <pubDate>Fri, 17 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.9: Tensors in Machine Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch9-tensors</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch9-tensors</guid>
      <description><![CDATA[An introduction to Tensors, the fundamental data structure of Machine Learning and Deep Learning, covering 0D to 5D tensors with practical examples.]]></description>
      <pubDate>Fri, 17 Jul 2026 18:25:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.9: Address Resolution Protocol (ARP)]]></title>
      <link>https://blog.ayuslh.in/networking/ch9-address-resolution-protocol-arp</link>
      <guid>https://blog.ayuslh.in/networking/ch9-address-resolution-protocol-arp</guid>
      <description><![CDATA[Understanding the Address Resolution Protocol (ARP), how it acts as a link between IP addresses and MAC addresses, and how devices use it to communicate.]]></description>
      <pubDate>Fri, 17 Jul 2026 05:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.8: Machine Learning Development Life Cycle (MLDLC)]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch8-machine-learning-development-life-cycle</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch8-machine-learning-development-life-cycle</guid>
      <description><![CDATA[A comprehensive, 9-step roadmap to building end-to-end Machine Learning products, taking you from problem framing all the way to cloud deployment and testing.]]></description>
      <pubDate>Thu, 16 Jul 2026 22:32:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.7: Challenges in Machine Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch7-challenges-in-machine-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch7-challenges-in-machine-learning</guid>
      <description><![CDATA[A comprehensive look at the top 10 challenges you will face when building real-world machine learning models, from data collection issues to deployment costs.]]></description>
      <pubDate>Thu, 16 Jul 2026 21:36:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.6: Instance-Based vs. Model-Based Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch6-instance-vs-model-based-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch6-instance-vs-model-based-learning</guid>
      <description><![CDATA[Understanding how Machine Learning algorithms learn from data by either memorizing instances or generalizing through mathematical models.]]></description>
      <pubDate>Thu, 16 Jul 2026 21:28:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.5: Online Machine Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch5-online-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch5-online-learning</guid>
      <description><![CDATA[A deep dive into Online Learning, how models learn incrementally in production, use cases like concept drift and out-of-core learning, and the associated risks.]]></description>
      <pubDate>Thu, 16 Jul 2026 20:46:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.4: Batch vs. Online Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch4-batch-vs-online-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch4-batch-vs-online-learning</guid>
      <description><![CDATA[Understanding how Machine Learning models are trained for production environments, with a deep dive into Batch (Offline) Learning and its limitations.]]></description>
      <pubDate>Thu, 16 Jul 2026 07:00:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.3: Types of Machine Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch3-types-of-machine-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch3-types-of-machine-learning</guid>
      <description><![CDATA[An in-depth look at the four main types of Machine Learning: Supervised, Unsupervised, Semi-supervised, and Reinforcement Learning.]]></description>
      <pubDate>Thu, 16 Jul 2026 06:50:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.2: AI vs. ML vs. Deep Learning]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch2-ai-vs-ml-vs-dl</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch2-ai-vs-ml-vs-dl</guid>
      <description><![CDATA[Unpacking the differences between Artificial Intelligence, Machine Learning, Deep Learning, and Reinforcement Learning using a game bot analogy.]]></description>
      <pubDate>Thu, 16 Jul 2026 06:40:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.1: What is Machine Learning?]]></title>
      <link>https://blog.ayuslh.in/machine-learning/ch1-what-is-machine-learning</link>
      <guid>https://blog.ayuslh.in/machine-learning/ch1-what-is-machine-learning</guid>
      <description><![CDATA[An original deep dive into the paradigm shift of machine learning, why traditional programming fails for complex problems, and the catalysts driving the AI revolution today.]]></description>
      <pubDate>Thu, 16 Jul 2026 06:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.8: IP Address vs. MAC Address]]></title>
      <link>https://blog.ayuslh.in/networking/ch8-ip-vs-mac-address</link>
      <guid>https://blog.ayuslh.in/networking/ch8-ip-vs-mac-address</guid>
      <description><![CDATA[Understanding the key differences between an IP Address and a MAC Address, and why a network requires both to successfully route data.]]></description>
      <pubDate>Thu, 16 Jul 2026 05:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.7: Understanding MAC Addresses]]></title>
      <link>https://blog.ayuslh.in/networking/ch7-mac-addresses</link>
      <guid>https://blog.ayuslh.in/networking/ch7-mac-addresses</guid>
      <description><![CDATA[What is a MAC address, how is it structured, and why do we need it if we already have IP addresses?]]></description>
      <pubDate>Thu, 16 Jul 2026 04:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.6: Putting It All Together (Routing In Action)]]></title>
      <link>https://blog.ayuslh.in/networking/ch6-routing-in-action</link>
      <guid>https://blog.ayuslh.in/networking/ch6-routing-in-action</guid>
      <description><![CDATA[A practical walkthrough of how IPs, Subnet Masks, and Default Gateways work together to move data across local and remote networks.]]></description>
      <pubDate>Sun, 12 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.5: The Default Gateway (Your Network's Exit Door)]]></title>
      <link>https://blog.ayuslh.in/networking/ch5-default-gateway</link>
      <guid>https://blog.ayuslh.in/networking/ch5-default-gateway</guid>
      <description><![CDATA[What happens when your computer needs to talk to a device on a completely different network? It sends the data through the Default Gateway.]]></description>
      <pubDate>Sun, 12 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.4: Subnet Masks and the 'Same Network' Test]]></title>
      <link>https://blog.ayuslh.in/networking/ch4-subnet-masks</link>
      <guid>https://blog.ayuslh.in/networking/ch4-subnet-masks</guid>
      <description><![CDATA[Discover how your computer uses Subnet Masks and bitwise AND operations to figure out if another device is on the local network or far away on the internet.]]></description>
      <pubDate>Sun, 12 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.3: Networks, Hosts, and CIDR Notation Explained]]></title>
      <link>https://blog.ayuslh.in/networking/ch3-networks-hosts-cidr</link>
      <guid>https://blog.ayuslh.in/networking/ch3-networks-hosts-cidr</guid>
      <description><![CDATA[Demystifying how IP addresses are structured into Network and Host portions, and understanding CIDR notation like /24 and /16.]]></description>
      <pubDate>Sun, 12 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.2: Understanding the Structure of an IP Address]]></title>
      <link>https://blog.ayuslh.in/networking/ch2-structure-of-an-ip-address</link>
      <guid>https://blog.ayuslh.in/networking/ch2-structure-of-an-ip-address</guid>
      <description><![CDATA[A beginner-friendly guide to the structure of an IPv4 address, octets, dotted quad notation, and binary representation.]]></description>
      <pubDate>Wed, 08 Jul 2026 18:30:00 GMT</pubDate>
    </item>
    <item>
      <title><![CDATA[Ch.1: What Happens When You Do an HTTP Request?]]></title>
      <link>https://blog.ayuslh.in/networking/ch1-what-happens-http-request</link>
      <guid>https://blog.ayuslh.in/networking/ch1-what-happens-http-request</guid>
      <description><![CDATA[What actually happens when you type a URL into your browser and hit enter: DNS resolution, the TCP handshake, IP packets, and MAC addresses, explained step by step.]]></description>
      <pubDate>Wed, 08 Jul 2026 18:30:00 GMT</pubDate>
    </item>
  </channel>
</rss>