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

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## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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:…

Continue Reading## 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…

Continue Reading## 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]…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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,…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

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