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

Continue Reading# loss

## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## 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…

Continue Reading## Loss and Loss Functions for Training Deep Learning Neural Networks

Neural networks are trained using stochastic gradient descent and require that you choose a loss function when designing and configuring…

Continue Reading## Review: RetinaNet — Focal Loss (Object Detection)

Review: RetinaNet — Focal Loss (Object Detection)One-Stage Detector, With Focal Loss and RetinaNet Using ResNet+FPN, Surpass the Accuracy of Two-Stage Detectors, Faster R-CNNSH…

Continue Reading## Get Started with PyTorch – Learn How to Build Quick & Accurate Neural Networks (with 4 Case Studies!)

A PyTorch implementation of a neural network looks exactly like a NumPy implementation. The goal of this section is to…

Continue Reading## Advanced Keras — Constructing Complex Custom Losses and Metrics

Advanced Keras — Constructing Complex Custom Losses and MetricsEyal ZakkayBlockedUnblockFollowFollowingJan 10Photo Credit: Eyal ZakkayTL;DR — In this tutorial I cover a simple trick that will allow…

Continue Reading## From raw images to real-time predictions with Deep Learning

From raw images to real-time predictions with Deep LearningFace expression recognition using Keras, Flask and OpenCVJonathan OheixBlockedUnblockFollowFollowingJan 7Photo by Peter Lloyd on UnsplashIn…

Continue Reading## Review: MultiPath / MPN — 1st Runner Up in 2015 COCO Detection & Segmentation (Object Detection / Instance Segmentation)

Review: MultiPath / MPN — 1st Runner Up in 2015 COCO Detection & Segmentation (Object Detection / Instance Segmentation)Multiple network layers, foveal…

Continue Reading## Custom TensorFlow Loss Functions for Advanced Machine Learning

Custom TensorFlow Loss Functions for Advanced Machine LearningAnd few-shot transfer learning exampleHaihan LanBlockedUnblockFollowFollowingJan 2In this article, we’ll look at:The use of custom…

Continue Reading## Intuitions on L1 and L2 Regularisation

Here’s a primer on norms:1-norm (also known as L1 norm)2-norm (also known as L2 norm or Euclidean norm)p-normA linear regression model…

Continue Reading## Text Generation Using Recurrent Neural Networks

It has an average accuracy of 0.6245 and loss of 1.25 over 5 randomly sampled test sequences.Yes, but to talk…

Continue Reading## Applying GANs to Super Resolution

The common loss function used for this is the MSE (Mean Squared Error) between the network output patch and the…

Continue Reading## Multi-class classification with focal loss for imbalanced datasets

This tutorial will show you how to apply focal loss to train a multi-class classifier model given highly imbalanced datasets.BackgroundLet’s…

Continue Reading## Physics-guided Neural Networks (PGNNs)

They present two approaches for this: (1) using physics theory, they calculate additional features (feature engineering) to feed into the…

Continue Reading## Will dropout regularization prevents your model to overfit?

We will dive in into the implementation of dropouts and prove if it will prevent overfitting.900 images of clothing (greyscale)…

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