# loss

## 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…

## Coding a 2 layer neural network from scratch in Python

We just ran our input data through the network and produced Yh, an output. The logical next step is to…

## Pix2Pix

Pix2PixConnor ShortenBlockedUnblockFollowFollowingJan 29Shocking result of Edges-to-Photo Image-to-Image translation using the Pix2Pix GAN AlgorithmThis article will explain the fundamental mechanisms of…

## How to Choose Loss Functions When Training Deep Learning Neural Networks

Deep learning neural networks are trained using the stochastic gradient descent optimization algorithm. As part of the optimization algorithm, the…

## Build a Recurrent Neural Network from Scratch in Python – An Essential Read for Data Scientists

Or simply pre-define the number of epochs.   Step 2. 1: Check the loss on training data We will do…