We just ran our input data through the network and produced Yh, an output. The logical next step is to…
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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 ReadingHow 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 ReadingBuild 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 ReadingLoss 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 ReadingReview: 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 ReadingGet 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 ReadingAdvanced 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 ReadingFrom 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 ReadingReview: 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 ReadingCustom 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 ReadingIntuitions 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 ReadingText 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 ReadingApplying 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 ReadingMulti-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 ReadingPhysics-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 ReadingWill 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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