# Start-Tech Academy Blogs

Newsletter

## Introduction to Linear Regression

Linear regression is a statistical method for estimating the relationship between two variables. With an understanding of linear regression, you can make predictions about how changing one variable will affect another.

## Introduction to Spark with Python: Spark Architecture and Components Explained in Detail

Great introduction to Spark with Python. Must - have material to learn basic Spark data analysis

## Introduction to Attention Mechanism in Deep Learning â€” ELI5 Way

Here you can find a simple explanation to Attention Mechanism in Deep Learning.

## Long Short Term Memory and Gated Recurrent Unitâ€™s Explained â€” ELI5 Way

I will try to explain how I can use LSTMs and GRUs in order to implement a very basic feed forward network that can classify characters in a training set.

## Recurrent Neural Networks (RNN) Explained â€” the ELI5 way

A blog about machine learning and other interesting deep learning projects

## Building a Feedforward Neural Network using Pytorch NN Module

This is a tutorial for Pytorch beginners on how to build a feedforward neural network. The tutorial starts with a linear regression model and ends at a 2-layer feedforward neural network.

## Demystifying Different Variants of Gradient Descent Optimization Algorithm

Gradient descent is the most popular optimization algorithm used in Deep Learning. It's used to calculate parameters for many different kinds of architectures, but there are also many different variants of it. The purpose of this post is not to explain what gradient descent is

## Implementing Different Variants of Gradient Descent Optimization Algorithm in Python using Numpy

Gradient descent (GD) is an optimization algorithm that was derived by solving the quadratic programming (QP). A set of input values, an initial value for the output value, and its cost are required to implement GD, which is specified

## There is No Noise â€” Only Bias

In this article, we will explore a number of issues around noise in machine learning and deep learning. We will see what causes the noise and how to get rid of it.

## KNNImputer: A robust way to impute missing values (using Scikit-Learn)

A proof of concept creation of a missing value imputing system using Scikit-Learn models.