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ALAnanya Lakshmi Bontalaananyas-blog.hashnode.devFeb 7 · 2 min read

What are Loss Functions?

A nice way to think about machine learning is to compare it to how we learn from our mistakes. In most ML setups, a model makes a guess, checks how wrong it was, and then tries to do a little better n

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VSVortana Sayvsaytech.hashnode.devFeb 2 · 4 min read

Entry #2A: Designing Learning Problems

I hit pause five minutes into Lecture 2 of CS230. Kian was already deep into a case study on day/night classification, but my notes were filling up with question marks. Not about the code—about the premise. The lecture assumed I already understood wh...

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JEJim Efoundational-ai-series-begins.hashnode.devDec 25, 2025 · 19 min read

Module 2: The Decision Process

We have established that W is a measure of how important X is to our final decision. In the scenario we ran in module 1, I manually assigned the weights since we were only dealing with 2 inputs. Imagine we were dealing with hundreds of thousands of i...

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AOAbdirashid Omar Matansimpleml.hashnode.devSep 6, 2025 · 9 min read

The Simplest Algorithm You Can Think About: KNN

Introduction When people hear machine learning, they often imagine massive neural networks powering self-driving cars or chatbots like ChatGPT. But the journey doesn’t start with deep learning — it starts with something much simpler. In fact, one of ...

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UNUnaiza Noumanconvolutionalneuralnetworks.hashnode.devAug 24, 2025 · 6 min read

Demystifying Convolutional Neural Networks: The Brains Behind Computer Vision

Introduction As we delve deeper into machine learning, we encounter tasks that require machines not just to make decisions, but to “see.” This is where Convolutional Neural Networks (CNNs) come into play, a specialized subset of deep learning models ...

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KSKrupa Sawantkrupasawant.hashnode.devAug 6, 2025 · 8 min read

Deep Learning for Coders with fastai and PyTorch: My Journey Through Chapters 4–7

When you first see a deep learning model recognize a cat or a handwritten number, it feels like magic. But in Deep Learning for Coders with fastai and PyTorch, Chapters 4–7 take you from that magic moment to fully understanding how it works under the...

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KSKrupa Sawantkrupasawant.hashnode.devAug 6, 2025 · 5 min read

How Neural Networks Tackle Regression, Multiclass, and Multilabel Problems

Neural networks are like pro gamers — they can play different games with the same brain.The rules change depending on the game, but the process is always: Guess → Check → Learn → Try Again. Here’s the ultimate side-by-side so you can see exactly ho...

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TRTrinita Roynlp-nerd.hashnode.devMay 30, 2025 · 6 min read

Machine Learning Fundamentals

Once a shadowy realm within academic circles and research papers, Machine Learning (ML) has emerged as a driving force behind today's technological marvels. From the helpful voices of our smart assistants to the vigilant eyes of fraud detection syste...

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TGTJ Gokkentjgokken.comApr 25, 2025 · 9 min read

Mean Absolute Error (MAE): The "No Drama" Loss Function

Today, we're diving into Mean Absolute Error (MAE) - the straightforward, no-nonsense member of the loss function family. The Problem with MBE (A Quick Recap) Remember how MBE tells us if our model is an optimist or pessimist? Well, that's useful, bu...

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TGTJ Gokkentjgokken.comApr 3, 2025 · 6 min read

Understanding Mean Bias Error in Machine Learning

Introduction Welcome to the first installment of our "ML Fundamentals: Loss Functions" series! Today, we're diving into Mean Bias Error (MBE), the unsung hero of bias detection in machine learning models. When building machine learning models, partic...

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#loss-functions

Explore Hashnode

Trending tags this week

What are Loss Functions?

Entry #2A: Designing Learning Problems

Module 2: The Decision Process

The Simplest Algorithm You Can Think About: KNN

Demystifying Convolutional Neural Networks: The Brains Behind Computer Vision

Deep Learning for Coders with fastai and PyTorch: My Journey Through Chapters 4–7

How Neural Networks Tackle Regression, Multiclass, and Multilabel Problems

Machine Learning Fundamentals

Mean Absolute Error (MAE): The "No Drama" Loss Function

Understanding Mean Bias Error in Machine Learning