KKehinde Onasanya2d ago 路 13 min readLinear Regression From ScratchLinear regression is one of the fundamental algorithms in supervised machine learning. Its simplicity makes it an excellent starting point for understanding how models learn relationships from data, mkehindeonasanya.hashnode.dev
AAkshay DubeyOct 23 路 9 min readThe Moment Calculus Finally Made Sense: Gradient Descent Explained SimplyIf you've read the previous articles and made it all the way here, thank you for the patience. Cheers! 馃帀 In the last stop in Algo City, we learned how an error function tells us how good or bad our lbinary-search-beyond-just-searching.hashnode.dev
DDivyajot KaurSep 26 路 5 min readPart 23: Types of Gradient Descent: Batch, Mini-Batch & StochasticIn the previous blog, we learned about Gradient Descent鈥攁n optimization algorithm that helps a machine learning model reduce its error by gradually adjusting its weights. But there is an important queai-beginners-journey.hashnode.dev
WWei RuiSep 24 路 12 min readA Software Engineer's Intuition for Gradient Descent, Part 2: The Step SizeIn Part 1 we built gradient descent around one update rule: $$\theta := \theta - \alpha \nabla f(\theta)$$ Almost all of that article was about the gradient 鈭噁(胃), the part that tells us which way is intuitiveml.hashnode.dev
WWei RuiSep 24 路 9 min readA Software Engineer's Intuition for Gradient DescentFor those of us who have spent years building deterministic software systems, the machine learning realm can feel like a parallel universe. In traditional software engineering, we write the exact ruleintuitiveml.hashnode.dev
DDivyajot KaurSep 21 路 5 min readPart 22: Gradient Descent: How Neural Networks Find Better WeightsImagine you are standing on top of a mountain, but it's completely dark. 馃寵 Your goal is to reach the lowest point in the valley. You can't see the entire path, so what would you do? You might take a ai-beginners-journey.hashnode.dev
TTharunAug 22 路 10 min readPolynomial Regression: Teaching a Line to BendTake our best straight-line model so far, the one that fit our five-order table perfectly. Now widen the range a bit. Some orders close by, a couple kilometers away. Some far, ten or twelve kilometers1tharunpandya.hashnode.dev
TTharunAug 19 路 10 min readFeature Scaling in Machine Learning: Why It MattersTake the exact same model, the exact same data, the exact same code. Change one thing: write the distance column in meters instead of kilometers. Nothing else moves. Run it, and the model doesn't just1tharunpandya.hashnode.dev
TTharunAug 16 路 9 min readMultiple Linear Regression: When One Input Isn't EnoughPicture two different orders. Both are exactly 3 kilometers away from the restaurant. One arrives in 18 minutes. The other takes 35 minutes. If distance is the only thing our model looks at, it has no1tharunpandya.hashnode.dev
EEva J PatelAug 15 路 33 min readHow Gradient Descent Works: The Math Behind Machine LearningWhen you train a machine learning model, you might write something as simple as: model.fit(X, y) and suddenly your model can make predictions. But there's a big question hiding behind that one line: 1evapatel123.hashnode.dev
