Coding Activities by Bhargav Venkat

Image Annotation with Shapes and Measurements

This activity involves annotating an image using OpenCV by drawing rectangles and circles to highlight regions of interest, connecting them with a line, and visualizing the image height using bi-directional arrows. Text annotations are added for clarity, making the image informative and visually structured.

Rotating and Adjusting Brightness

Rotate an image by 45 degrees and adjust its brightness to see the effects of basic arithmetic operations on images.

Color Conversions and Cropping

Convert an image from BGR to RGB and grayscale, then crop a region of interest.

Save & grayscale with OpenCv

In this activity, students will load an image, convert it to grayscale, resize it to a standard size (224x224), display the processed image, and optionally save it based on key press.

Display & resize image with OpenCV

In this activity, students will learn how to load an image using OpenCV, display it in a resizable window, and adjust the window size. They will also explore the properties of an image such as dimensions and channels.

House Price Prediction using Keras

Create a neural network that can help you predict the house price using Keras. All the data is numerical, free of null values, so data preprocessing is not required.

Try Different Activation functions

Open the link to the page given below. Consider you have been given a dataset - Pima Indians onset of diabetes dataset. This has patient medical record data for Pima Indians and whether they had an onset of diabetes of five years. It is a binary class classification problem (onset of diabetes as one or not as 0). This dataset consists of 3 features/columns, including diabetes onset. Now, set the training to testing ratio to 80%. Now add two hidden layers with 4 and 6 neurons respectively and create a google document to enter values of testing loss for the following entries - 1) Sigmoid activation function 2) Tanh activation function

Forward Propagation

Have a look at this image given below. Now consider the following values and then calculate the value of the Output Layer using forward propagation. Consider that this is a Classification problem, and use the activation function accordingly. Here are the values mentioned - X1 = 20 X2 = 30 X3 = 12 W1 = W2 = 0.6 W3 = 0.3 W4 = 0.7 Submit the link of a google document that will consist of an image of all the calculations done for getting the final output.

Try Different Layers

Open the link to the page given below. Consider you have been given a dataset - Pima Indians onset of diabetes dataset. It describes patient medical record data for Pima Indians and whether they had an onset of diabetes within five years. As such, it is a binary classification problem (onset of diabetes as one or not as 0). This dataset consists of a total of 3 features/columns, including diabetes onset. Now, set the training to testing ratio to 80%. Now try adding hidden layers and neurons in each hidden layer and create a google document to enter values of testing loss for the following entries - 1) 1 Hidden layer (3 neurons) 2) 2 Hidden layers (3 neurons) 3) 3 Hidden layers (3 neurons) 4) 4 Hidden layers (3 neurons) 5) 3 Hidden layers with eight neurons 6) 3 Hidden layers with six neurons

My Neural Network

Open the notebook attached to this activity, and based on the given image and problem, answer the questions regarding the neural network that you can create. Submit this updated notebook link in the submission section.

AI around me!

Mention the products of AI around you in different domains. Let’s see how many you can guess? Make a copy of this file and then edit it to submit your answers.

Things Translator

Take a picture of something to hear how to say it in a different language. This experiment uses Artificial Intelligence to detect an image and say it in multiple languages.