What is Confusion Matrix in Machine Learning

Not only human beings but also the machine learning models may get confused !! After all Artificial Intelligence mimics a human brain, isn’t it?(pun intended). Imagine yourself as a machine learning engineer and suppose you trained a machine learning classification model successfully today. After the model is trained you checked the accuracy which is 93.0%. Wow … Continue reading What is Confusion Matrix in Machine Learning

Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Great Book !! Well organised, Concise and Complete !!This book summarises the vast and complex topic of deep learning in a textbook, by some of the leaders in the field.What has been most valuable is, seeing how it all fits together.There are lots of books, blogs, and videos out there, but this is one of … Continue reading Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Difference Between Batch, Mini-Batch and Stochastic Gradient Descent

Gradient Descent is one the key algorithm used in Machine Learning. While training machine learning model, we require an algorithm to minimize the value of loss function. Gradient Descent is one of the optimization algorithm , that is used to minimize the loss. There are mainly three types of Gradient Descent algorithm1. Batch Gradient DescentBatch … Continue reading Difference Between Batch, Mini-Batch and Stochastic Gradient Descent

How to write command-line arguments using argparse in python

If you want to run a .py python file from command line and you also want to pass the argument using command line you can use argparse library. This can be done as below: import argparse # Command Line arguments argp = argparse.ArgumentParser() argp.add_argument('--my_var', dest="my_var", action="store", type=int, default=5) params = argp.parse_args() myvar= params.my_var print(myvar) Then … Continue reading How to write command-line arguments using argparse in python

Using torchvision transforms for data augmentation

Transforms are common image transformations. torchvision transforms are used to augment the data with scaling, rotations, mirroring, cropping etc. Transforms are common image transformations.  Let us create some possible transforms like RandomRotation , Resize, RandomResizedCrop etc. Below is the code to use transforms for the training, validation, and testing sets transforms.Compose([transforms.Resize(224),transforms.CenterCrop(64),transforms.ToTensor()]) Most neural networks expect the … Continue reading Using torchvision transforms for data augmentation