Highlights: Hello and welcome to our new blog post. So far, we have mainly worked with very simple datasets. To train our models we used linear regression and logistic regression models and for that purpose, we artificially created very easy toy data sets such as blobs. However, in order to fully utilize the power of Deep Learning and neural networks, we will have to work with the larger dataset. Now it is a great time…
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Highlights: In Machine Learning, a backpropagation algorithm is used to compute the loss for a particular model. The most common starting point is to use the techniques of single-variable calculus and understand how backpropagation works. However, the real challenge is when the inputs are not scalars but of matrices or tensors. In this post [1], we will learn how to deal with inputs like vectors, matrices, and tensors of higher ranks. We will understand how…
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Highlights: In this post, we will introduce computation graphs – a systematic and easy way to represent our linear model. A computation graph is a fundamental concept used to better understand and calculate derivatives of gradients and cost function in the large chain of computations. Furthermore, we will conduct an experiment in Microsoft Excel where we will manually calculate gradients and derivatives of our linear model. Finally, we will show you how to calculate gradients…
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Highlight: In this post we are going to explain what a Liner regression is. After covering the basic theory behind Linear regression, we are going to code a simple linear regression model in Python using PyTorch library. So, let’s begin. Tutorial overview: What is a linear model? Building a linear model Linear regression Loss function Calculating the Mean Squared Error (MSE) Cost graph Gradient descent Linear regression in PyTorch Intuitive implementation of Linear regression in…
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