Search results for "Convolutional Neural Network"

Examples from the MNIST dataset

Handwritten Digit Recognition using Convolutional Neural Networks in Python with Keras

A popular demonstration of the capability of deep learning techniques is object recognition in image data. The “hello world” of object recognition for machine learning and deep learning is the MNIST dataset for handwritten digit recognition. In this post you will discover how to develop a deep learning model to achieve near state of the […]

Continue Reading 309
Crash Course in Convolutional Neural Networks for Machine Learning

Crash Course in Convolutional Neural Networks for Machine Learning

Convolutional Neural Networks are a powerful artificial neural network technique. These networks preserve the spatial structure of the problem and were developed for object recognition tasks such as handwritten digit recognition. They are popular because people are achieving state-of-the-art results on difficult computer vision and natural language processing tasks. In this post you will discover […]

Continue Reading 50
A Gentle Introduction to Convolutional Layers for Deep Learning Neural Networks

How Do Convolutional Layers Work in Deep Learning Neural Networks?

Convolutional layers are the major building blocks used in convolutional neural networks. A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a […]

Continue Reading 19
Scatter Plot of Binary Classification Dataset with 1 to 100 Class Imbalance

How to Develop a Cost-Sensitive Neural Network for Imbalanced Classification

Deep learning neural networks are a flexible class of machine learning algorithms that perform well on a wide range of problems. Neural networks are trained using the backpropagation of error algorithm that involves calculating errors made by the model on the training dataset and updating the model weights in proportion to those errors. The limitation […]

Continue Reading 8
How to Evaluate Generative Adversarial Networks

How to Evaluate Generative Adversarial Networks

Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. […]

Continue Reading 0
Examples of Class Leakage in an Image Generated by Partially Trained BigGAN

A Gentle Introduction to BigGAN the Big Generative Adversarial Network

Generative Adversarial Networks, or GANs, are perhaps the most effective generative model for image synthesis. Nevertheless, they are typically restricted to generating small images and the training process remains fragile, dependent upon specific augmentations and hyperparameters in order to achieve good results. The BigGAN is an approach to pull together a suite of recent best […]

Continue Reading 2
GANs in Action

9 Books on Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled “Generative Adversarial Networks.” Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books […]

Continue Reading 10
Example of High-Quality Generated Faces Using the StyleGAN

A Gentle Introduction to StyleGAN the Style Generative Adversarial Network

Generative Adversarial Networks, or GANs for short, are effective at generating large high-quality images. Most improvement has been made to discriminator models in an effort to train more effective generator models, although less effort has been put into improving the generator models. The Style Generative Adversarial Network, or StyleGAN for short, is an extension to […]

Continue Reading 10
Pix2Pix GAN Translation of Product Sketches of Shoes to Photographs

A Gentle Introduction to Pix2Pix Generative Adversarial Network

Image-to-image translation is the controlled conversion of a given source image to a target image. An example might be the conversion of black and white photographs to color photographs. Image-to-image translation is a challenging problem and often requires specialized models and loss functions for a given translation task or dataset. The Pix2Pix GAN is a […]

Continue Reading 2
Example of 100 LSGAN Generated Handwritten Digits After 20 Training Epochs

How to Develop a Least Squares Generative Adversarial Network (LSGAN) in Keras

The Least Squares Generative Adversarial Network, or LSGAN for short, is an extension to the GAN architecture that addresses the problem of vanishing gradients and loss saturation. It is motivated by the desire to provide a signal to the generator about fake samples that are far from the discriminator model’s decision boundary for classifying them […]

Continue Reading 4