# mehran@mldawn.com

## Back-propagation with Cross-Entropy and Softmax

What will you learn? This post is also available to you in this video, should you be interested ðŸ˜‰ In our previous post, we talked about the derivative of the softmax function with respect to its input. We indeed beautifully dissect ed the math and got comfortable with it! In this post, we will go …

## COVID-19 Analysis

Studying and Predicting the Progress of COVID-19 using Pandas and ARIMA COVID-19 has been around for nearly 4 months since the outbreak. In this notebook, we will study some of the useful statistics regarding number of confirmed/deaths/recovered cases as a function of time per each country/region. We will use the the dataset that has been …

## Deriving the Gradient Descent Rule (PART-2)

What Will You Learn? In our previous post, we have talked about the meaning of gradient descent and how it can help us update the parameters of our Artificial Neural Network (ANN). In this post we will actually mathematically derive the update rule using the concept of gradient descent. We will also look at a …

## The Derivative of Softmax(z) Function w.r.t z

What will you learn? Ask any machine learning expert! They will all have to google the answer to this question: “What was the derivative of the Softmax function w.r.t (with respect to) its input again?” The reason behind this forgetfulness is that Softmax(z) is a tricky function, and people tend to forget the process of …

## Deriving the Gradient Descent Rule (PART-1)

The Gradient Descent Rule https://www.youtube.com/watch?v=gYqG4OT2Kj4 When training a model, we strive to minimize a certain error function (). This error function gives us an indication as to how well is our model doing on our training data. So, in general, the lower it is, the better our model is doing on the training set. Make …

## Reproducibility in Pytorch

What is Reproducibility All About? As a computer scientist, or as an academician, you do experiments with a bunch of algorithms. Let’s say you have coded a machine learning algorithm, like an Artificial Neural Network, and after doing your experiments with different datasets, you have found out that the best type of neural network has …

## What is the Delta Rule? (Part-2)

What We Have Learned So Far … So far, we have learned that the Delta rule guarantees to converge to a model that fits our data the best! It just so happens that the best fit might be a terrible model but still it is the best that the Delta rule has been able to …

## What is the Delta Rule? (Part-1)

The Beauty that is the Delta Rule In general, there are 2 main ways to train an Artificial Neural Network (ANN). In our previous post , I have told you about the popular perceptron rule that has been around for a long time. We also said that the perceptron training rule is guaranteed to converge …

## The Perceptron Training Rule

The Perceptron Training Rule It is important to learn the training process of huge neural networks. However, we need to simplify this by first understanding how a simple perceptron is trained, and how its weights are updated! Only then, will we be able to understand the dynamics of complicated and monstrous neural networks, such as …

## ECML-PKDD-2019: Elliptical Basis Function Data Descriptor (EBFDD) for Anomaly Detection

ECML-PKDD-2019 on EBFDD networks for Anomaly Detection This paper introduces the Elliptical Basis Function Data Descriptor (EBFDD) network, a one-class classification approach to anomaly detection based on Radial Basis Function (RBF) neural networks. The EBFDD network uses elliptical basis functions, which allows it to learn sophisticated decision boundaries while retaining the advantages of a shallow …