I recently deployed my portfolio site and wanted to try out github actions and this is my experience of automating the deployment.

This article is more focused on how you can use the GitHub actions and how easy it is to deploy your code to GitHub pages rather than the portfolio site code.So every time you make an update or build to your website ,the changes are automatically reflected and this automated deploying process makes work much faster.

The way GitHub action works is you create actions in your repositories by creating one or more yaml files and these are…

Let’s learn Convolution!

In the previous article, we learned about how computers see and process images and the problems of manual feature extraction & finally understood the problems we face in image classification & concluded with the approach of learning visual features from data rather than hand engineering. This article will demonstrate How we can learn visual features with neural networks

In neural networks series, we learned about fully connected or dense neural networks where you can multiple hidden layers and each of these hidden layers are densely connected to the previous layer. …

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.

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…

In this article , we will learn about how we can calculate the gradient and how the network learns through back propagation. Following from the previous article regarding our discussion of neural networks and gradient descent.We talked about gradient and how it works for reducing the loss till we converge to a global minimum.

Let’s have a look at a network having a single hidden neuron and a single output.

How does a small change in (ex.:w2) affect the final loss J(W)?

Computing our gradient of our loss with respect to weight W2(which is the second weight between the output and the hidden layer). Our output can tell us how much a…

From the previous article , we learnt how a single neuron or perceptron works by taking the dot product of input vectors and weights,adding bias and then applying non-linear activation function to produce output.Now let’s take that information and see how these neurons build up to a neural network.

The perceptron or a single neuron is the fundamental building block of a neural network .The idea of a neuron is basic but essential .

Lets start understanding the forward propagation of information through a single neuron.

We define a sets of inputs to that neuron as x1 ,x2 …xn. And each of these inputs have a corresponding weight w1 …wn.What we can do is with each of these inputs and weights, we can multiply them correspondingly together and take a sum of all of them.We can take this summation which is a single number and pass it through what…

Shweta Kadam

Software Engineer and always a Learner! https://shwetarkadam.github.io/portfolio

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