Udacity PyTorch Challengers

In this post, I will explain ‘Bias-Variance Tradeoff’, ‘Regularization’, and ‘Learning rate decay’ in short. and tell you about my trial…

Loss function helps us to quantify how good/bad our current model is in predicting some value which it is trained to predict. This article…

“PyTorch are dynamic Neural Networks which are like tensors on steroids running in Python with strong GPU acceleration”

What are loss functions? How do they work? Where to use them?

As with any new subject or topic, it is important to lay down the basics to set the tone, line of thought and context. The basics may or…

If you have just started learning Machine Learning, you will often hear this term called overfitting, but what exactly it is?

An artificial neural network is an interconnected group of nodes, similar to the vast network of neurons in a brain. It is the building…

This article is an introduction to transfer learning (TL) using PyTorch. I will illustrate the concept in simple terms and present the…

By Florin Cioloboc and Harisyam Manda — PyTorch Challengers

In the following paragraphs I’m going to motivate why you should consider using pre-trained models instead of creating one from scratch.

In neural networks, activation functions add nonlinearities to the output of the neuron and there are many activation functions. Before we…

Learn What and where and How to save your trained model using PyTorch and try what you learned yourself live.

With the revolution of artificial intelligence and deep learning, many built-in libraries such as Pytorch and tensorflow can be used to…

So, let’s say you work at a medical laboratory and you’ve been given the task to build an artificially intelligent solution that’s capable…

A PyTorch Scholarship Challenge student quip

Introduction