Osborne  Durgan

Osborne Durgan

1595230791

A Quick Grasp of Convolution Neural Networks (CNN)

Convolutional Neural Networks(CNN) is one of the popular Deep Artificial Neural Networks. CNN’s are made up of learnable weights and biases. CNN’s are very similar to ordinary neural networks but not exactly the same.

CNN’s are primarily used in image recognition, image clustering, and classification, object detection, etc…

Why CNN’s?

CNN’s is weight sharing, less complex, and occupies less memory.

Let’s take an MNIST data set image, and it’s passed to CNN and NN.

Assume on the CNN layer,10 filters of 5x5 size, then we have 5x5x10 +10(biases) =260 params.

Assume the image dimensions 784, and a NN layer of 250 neurons, then in Neural Network (NN) we have 784 x 260 + 1= 19601 params

So, CNN’s outperform NNs on conventional image recognition tasks and many other tasks.

The idea behind the working of CNN

convolution operation in computer vision is biologically inspired by the brain’s visual cortex. The connectivity pattern of CNN resembles the structure of the animal visual cortex.

If an image is passed to the visual cortex, then the cortex processes that information through the segments/layers. The brain extracts information from every segment/layer. The first layers learn representations such as edges or color while the intermediate-level layers learn intermediate abstract representations such as object parts and finally, high-level layers learn full objects like cat’s faces. with an increase in the levels of abstractions, inferences become more clear. Thus, the brain makes decisions from the information it has learned through all layers.

#mnist #convolution-neural-net #code #neural-networks

What is GEEK

Buddha Community

A Quick Grasp of Convolution Neural Networks (CNN)

Eran Feit

1643186535

Hi,

I attached a link for a nice tutorial for classifying between several fruits and vegetables.

This is the link : https://youtu.be/w5T86Z3lod0

Eran

Osborne  Durgan

Osborne Durgan

1595230791

A Quick Grasp of Convolution Neural Networks (CNN)

Convolutional Neural Networks(CNN) is one of the popular Deep Artificial Neural Networks. CNN’s are made up of learnable weights and biases. CNN’s are very similar to ordinary neural networks but not exactly the same.

CNN’s are primarily used in image recognition, image clustering, and classification, object detection, etc…

Why CNN’s?

CNN’s is weight sharing, less complex, and occupies less memory.

Let’s take an MNIST data set image, and it’s passed to CNN and NN.

Assume on the CNN layer,10 filters of 5x5 size, then we have 5x5x10 +10(biases) =260 params.

Assume the image dimensions 784, and a NN layer of 250 neurons, then in Neural Network (NN) we have 784 x 260 + 1= 19601 params

So, CNN’s outperform NNs on conventional image recognition tasks and many other tasks.

The idea behind the working of CNN

convolution operation in computer vision is biologically inspired by the brain’s visual cortex. The connectivity pattern of CNN resembles the structure of the animal visual cortex.

If an image is passed to the visual cortex, then the cortex processes that information through the segments/layers. The brain extracts information from every segment/layer. The first layers learn representations such as edges or color while the intermediate-level layers learn intermediate abstract representations such as object parts and finally, high-level layers learn full objects like cat’s faces. with an increase in the levels of abstractions, inferences become more clear. Thus, the brain makes decisions from the information it has learned through all layers.

#mnist #convolution-neural-net #code #neural-networks

Martin  Soit

Martin Soit

1602983880

Convolutional Neural Networks (CNN) Model from scratch for Object Detection

If you can’t explain it simply, you don’t understand it well enough - Einstein, the Man and His Achievement By G. J. Whitrow, Dover Press 1973.

CNN Model made from scratch, using the most popular Kaggle dataset  Fruits-360 and obtaining 98% accuracy.

Step 1- Importing Dataset From Kaggle to Google Colab

Login to your Kaggle account and go to My Account, and download Kaggle.json file by clicking on CREATE NEW API. Then on Google colab upload the same API by following this code gist

!pip install -q kaggle
from google.colab import files
files.upload()
#upload your kaggle.json kaggle api
  • Directly import the whole dataset to google colab and unzip the same
! mkdir ~/.kaggle
! cp kaggle.json ~/.kaggle/
! chmod 600 ~/.kaggle/kaggle.json
! kaggle datasets download -d moltean/fruits

! mkdir fruits
! unzip fruits.zip -d fruits

#image-recognition #cnn #data-science #convolutional-neural-net #neural-networks

CNN Series Part 1: How do computers see images?

In this article, we will learn about how computers see images & the issues faced while performing a computer vision task. We will see how deep learning comes into the picture & how with the power of neural networks, we can build a powerful computer vision system capable of solving extraordinary problems.

Image for post

One example of how deep learning is transforming computer vision is facial recognition or face detection. On the top left, you can see the icon of the human eye which visually represents vision coming into the deep neural network in the form of images, pixels, videos & on the output on the bottom you can see a depiction of the human face or detection of the human face or this could also be recognizing different human faces or emotions on the face and also the key facial features, etc.

#convolution-neural-net #computer-vision #neural-networks #cnn #convolutional-network #series

Convolutional Neural Networks, Explained

A Convolutional Neural Network, also known as CNN or ConvNet, is a class of neural networks that specializes in processing data that has a grid-like topology, such as an image. A digital image is a binary representation of visual data. It contains a series of pixels arranged in a grid-like fashion that contains pixel values to denote how bright and what color each pixel should be.

Image for post

Figure 1: Representation of image as a grid of pixels (Source)

The human brain processes a huge amount of information the second we see an image. Each neuron works in its own receptive field and is connected to other neurons in a way that they cover the entire visual field. Just as each neuron responds to stimuli only in the restricted region of the visual field called the receptive field in the biological vision system, each neuron in a CNN processes data only in its receptive field as well. The layers are arranged in such a way so that they detect simpler patterns first (lines, curves, etc.) and more complex patterns (faces, objects, etc.) further along. By using a CNN, one can enable sight to computers.

Convolutional Neural Network Architecture

A CNN typically has three layers: a convolutional layer, a pooling layer, and a fully connected layer.

Image for post

Figure 2: Architecture of a CNN (Source)

Convolution Layer

The convolution layer is the core building block of the CNN. It carries the main portion of the network’s computational load.

This layer performs a dot product between two matrices, where one matrix is the set of learnable parameters otherwise known as a kernel, and the other matrix is the restricted portion of the receptive field. The kernel is spatially smaller than an image but is more in-depth. This means that, if the image is composed of three (RGB) channels, the kernel height and width will be spatially small, but the depth extends up to all three channels.

#computer-vision #writing-nn #convolution-network #cnn #fashion-mnist #neural networks

Philian Mateo

Philian Mateo

1604977629

10 Free Online Resources To Learn Convolutional Neural Networks

C onvolutional Neural Networks (CNNs) are one of the most important neural network algorithms in the present scenario. Tech giants like Google, Facebook, Amazon have been thoroughly using this neural network to perform and achieve a number of image-related tasks.

The  applications of CNNs mostly includes the field of computer vision for image recognition, object detection, among others, This neural network is also being used for video inputs, speech recognition, sentence modelling, etc. in NLP models and more.

Below, we have curated a list of 10 best free online resources, in no particular order, to learn  Convolutional Neural Networks (CNNs).


Convolutional Neural Networks

About: This course is a part of the Deep Learning Specialisation at Coursera. Here, you will learn how to build  convolutional neural networks and apply them to image data. You will understand how to build a CNN model, understand the recent variations, know how to apply  convolutional networks to visual detection as well as recognition tasks and more.

Know more here.

Introducing Convolutional Neural Networks

**About: **This tutorial is curated by the developers at Google. This tutorial, encompasses a brief introduction on  convolution neural networks (CNNs), how it works, including hands-on training. You will learn topics like ReLU, pooling, fully connected layers and more.

Know more here.

Convolution Neural Networks for Visual Recognition

**About: **This is a free course where you will learn about  convolution neural networks and how they can be used in visual recognition. The tutorial starts with an architecture overview and then moves into ConvNet layers such as normalisation layer, fully connected layer, etc. including its architectures, such as layer patterns, computational considerations and more.

Know more here.


#cnn neural network #cnns #convolutional neural networks