# loss

## A Beginner’s Guide to Focal Loss in Object Detection!

Introduction Object detection is one of the most widely studied topics in the computer vision community. It’s has been breaking…

## How to Reduce Computational Constraints using Momentum Contrast V2(Moco-v2) in PyTorch

IntroductionThe SimCLR paper explains how this framework benefits from larger models and larger batch sizes and can produce results comparable…

## A Gentle Introduction to Cross-Entropy for Machine Learning

Cross-entropy is commonly used in machine learning as a loss function. Cross-entropy is a measure from the field of information…

## A Gentle Introduction to Generative Adversarial Network Loss Functions

A Large-Scale Study, 2018. The result is better gradient information when updating the weights of the generator and a more…

## A Detailed Guide to 7 Loss Functions for Machine Learning Algorithms with Python Code

We can consider this as a disadvantage of MAE. Here is the code for the update_weight function with MAE cost:…

## Neural Networks: parameters, hyperparameters and optimization strategies

Well, if you think about a generic loss function with only one weight, the graphic representation will be something like…

## Deep Neural Networks from scratch in Python

The network can be applied to supervised learning problem with binary classification. Figure 1. Example of neural network architectureNotationSuperscript [l]…

## Training a Convolutional Neural Network from scratch

Time to get into it. We’ll pick back up where my introduction to CNNs left off. We were using a…

## Clearing air around “Boosting”

By Puneet Grover, Helping Machines Learn. Clearing Photo by SpaceX on UnsplashNote: Although this post is a little bit math oriented, still you can…

## Gradient Descent in Deep Learning

They don’t. First, neural networks are complicated functions, with lots of non-linear transformations thrown in our hypothesis function. The resultant…

## Estimators, Loss Functions, Optimizers —Core of ML Algorithms

Estimators, Loss Functions, Optimizers —Core of ML AlgorithmsJavaid NabiBlockedUnblockFollowFollowingMay 24In order to understand how a machine learning algorithm learns from…

## How to create a neural network from scratch in Python — Math & Code

For most functions, in fact we can’t know. Here, the trick comes from a theorem demonstrated by Kurt Hornik called…

## Understanding the 3 most common loss functions for Machine Learning Regression

Understanding the 3 most common loss functions for Machine Learning RegressionGeorge SeifBlockedUnblockFollowFollowingMay 20A loss function in Machine Learning is a…

## How to Generate Prediction Intervals with Scikit-Learn and Python

How to Generate Prediction Intervals with Scikit-Learn and PythonUsing the Gradient Boosting Regressor to show uncertainty in machine learning estimatesWill KoehrsenBlockedUnblockFollowFollowingMay…

## Detecting a simple neural network architecture using NLP for email classification

Detecting a simple neural network architecture using NLP for email classificationHyper parameter optimization in email classification. tannistha maitiBlockedUnblockFollowFollowingApr 19About a…

## How to build your first Neural Network to predict house prices with Keras

Congratulations!Summary: Coding up our first neural network required only a few lines of code:We specify the architecture with the Keras…

## Predictive Maintenance: detect Faults from Sensors with CNN

Predictive Maintenance: detect Faults from Sensors with CNNAn interesting approach with python code and graphic representationsMarco CerlianiBlockedUnblockFollowFollowingMar 30In Machine Learning the…

## Fraud detection with cost-sensitive machine learning

Let’s assume the following scenario. If a fraudulent transaction is not recognized by the system, the money is lost and…

## Better Understanding Negative Log Loss

But I was seeing the opposite effect. My next attempt at understanding the observed behavior was to use a sufficiently…

## Speeding Up and Perfecting Your Work Using Parallel Computing

Speeding Up and Perfecting Your Work Using Parallel ComputingA detailed guide of Python multiprocessing vs. PySpark mapPartitionYitong RenBlockedUnblockFollowFollowingMar 18In science,…

## Checklist for debugging neural networks

Erik Rippel has a great, colorful post on ‘Visualizing parts of Convolutional Neural Networks using Keras and Cats’4. Diagnose parametersNeural…

## Beating the Bookies with Machine Learning

I. e. the ‘payout’ the bookmaker sets for this game is 95%, meaning that the bookmaker will expect to make…

## How to use deep learning on satellite imagery — Playing with the loss function

“If the loss is well designed”? What does it actually mean?Loss functions are usually complex mathematical cost functions to be optimized…

## Analyzing my weight loss journey with machine learning

After I rescaled my features, these warnings went away and my algorithm was able to converge. By reducing my features…

## What To Optimize for? Loss Function Cheat Sheet

I would argue the validation loss is the most important. Validation loss is how we decide “model A is better…