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Downsampling and Upsampling in CNN

There is no doubt that convolution neural network gave a huge progress to computer vision sector and in this article I will walk with you in short journey with some of its concepts specially downsampling and upsampling in CNN.

Machine Learning (ML)

Inception V4 architecture

We have explored about a neural network architecture called Inception and understand in great detail its fourth version, the Inception V4 along with the architecture of InceptionV4 model.

Machine Learning (ML)

Multilayer Perceptrons vs CNN

We have explored the key differences between Multilayer perceptron and CNN in depth. Multilayer Perceptron and CNN are two fundamental concepts in Machine Learning. When we apply activations to Multilayer perceptrons, we get Artificial Neural Network (ANN) which is one of the earliest ML models.

Machine Learning (ML)

Calculate output size of Convolution

In this article, we have illustrated how to calculate the size of output in a convolution provided we have the dimensions of input data, kernel, stride and padding.

Machine Learning (ML)

Implementing CNN in Python with Tensorflow for MNIST digit recognition

In this article, we will develop and train a convolutional neural network (CNN) in Python using TensorFlow for digit recognifition with MNIST as our dataset. We will give an overview of the MNIST dataset and the model architecture we will work on before diving into the code.

Machine Learning (ML)

Neural Style Transfer using CNN

We demonstrate the easiest technique of Neural Style or Art Transfer using Convolutional Neural Networks (CNN). We use VGG19 as our base model and compute the content and style loss, extract features, compute the gram matrix, compute the two weights and generate the image with the other style

Machine Learning (ML)

Convolutional Neural Networks (CNN)

Convolutional Neural Network (CNN) is an neural network which extracts or identifies a feature in a particular image and is the basis of GoogleNet and VGG19 and used for object detection and classification. CNN has five basic components Convolution, ReLU, Pooling, Flattening and Full connection.

Machine Learning (ML)

Architecture of AlexNet and its current use

Alexnet is a Deep Convolutional Neural Network (CNN) for image classification that won the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. We see the architecture and compare it with GoogleNet and ResNet

Machine Learning (ML)

Evolution of CNN Architectures: LeNet, AlexNet, ZFNet, GoogleNet, VGG and ResNet

It all started with LeNet in 1998 and eventually, after nearly 15 years, lead to ground breaking models winning the ImageNet Large Scale Visual Recognition Challenge which includes AlexNet in 2012, ZFNet in 2013, GoogleNet in 2014, VGG in 2014, ResNet in 2015 to ensemble of previous models in 2016.

Machine Learning (ML)

VGG16 architecture

We have explored the VGG16 architecture in depth. VGGNet-16 consists of 16 convolutional layers and is very appealing because of its very uniform Architecture. Similar to AlexNet, it has only 3x3 convolutions, but lots of filters. It can be trained on 4 GPUs for 3 weeks.

Machine Learning (ML)

When to use Convolutional Neural Networks (CNN)?

Use CNN for data with a spatial relationship. Convolutional Neural Networks (CNNs) are designed to map image data to an output variable. They have proven so effective that they are the ready to use method for any type of prediction problem involving image data as an input

Machine Learning (ML)

Building a Convolution Neural Network (CNN) for handwritten digit recognition in Python using Keras

We built a Convolution Neural Network (CNN) for handwritten digit recognition from scratch in python. We will be using Keras API with TensorFlow backend and use handwritten digits dataset from Kaggle.