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A Dive into Deep Learning- Part 1

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The general interest in deep learning has peaked during the past few years. Projects like DeepMind’s AlphaGo shed a light on the power of AI when it comes to surpassing humans in board games. So…


Linear Regression: Mathematical Intuition

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Since the start of your data scientist journey, you have been commonly accustomed with this machine learning algorithm. Linear Regression as it is the basic and foremost machine learning algorithm we generally start with while analysing different regression problems. As the word linear says, the linear relationship between input variables(x) with the dependent output variable(y). Basically the linear regression analysis performs the task of predicticting the output variable by modelling or finding relationships between the independent variables(x). And the approach of finding the best ouput is by fitting the predicted line towards the best fit line.


Linear Regression

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As described by each words Linear (arranged in or extending along a straight or nearly straight line) Regression (measure of the relation between variables). Linear Regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, or features, and the other is considered to be a dependent variable, or target. In the field of machine learning Linear Regression is a considered a supervised learning task. A Linear Regression line has an equation of the form Y mX C, where X is the explanatory variable or feature variable and Y is the dependent variable or a target variable.


Logistic Regression for Classification - KDnuggets

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Before we understand more about Logistic Regression, let's first recap some important definitions which will give us a better understanding of the topic. Logistic Regression comes under Supervised Learning. Supervised Learning is when the algorithm learns on a labeled dataset and analyses the training data. These labeled data sets have inputs and expected outputs. Supervised learning can be further split into classification and regression. Classification is about predicting a label, by identifying which category an object belongs to based on different parameters.


What is momentum in a Neural network and how does it work?

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In a neural network, there is the concept of loss, which is used to calculate performance. The higher the loss, the poorer the performance of the neural network, that is why we always try to minimize the loss so that the neural network performs better. The process of minimizing loss is called optimization. An optimizer is a method that modifies the weights of the neural network to reduce the loss. Although several neural network optimizers exist, in this article we will learn about gradient descent with momentum and compare its performance with others.


Deep Learning Simplified: Feel and Talk like an Expert in Neural Networks

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The most recent breakthrough in deep learning research comes from OpenAI with two astonishing transformers -- GPT-3 and DALL-E [6], the former being an AI novelist and poet and the latter an AI designer and artist. With GPT-3 you can start a novel or a poem with a few sentences or paragraphs and ask the model to complete it. DALL-E model transforms text into many images.


Logistic Regression in Machine Learning (from Scratch !!)

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In this blog post, I would like to continue my series on "building from scratch." I will discuss a linear classifier called Logistic Regression. After the discussion of the theoretical concepts we will dive into the code. So, without a further adieu let's start the discussion with the basics of a classifier. A classifier is an estimator that assigns a class label to the input data point.


Linear Regression : decoded

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Everyone wants to try their hands on Machine Learning at some point of time in their software career. The first algorithm mostly all books and online courses starts with is the Linear regression. Linear arranged in a straight line. So, the idea of understanding the relationship between 2 variables by plotting a linear line is coined as linear regression. Let us take an example, Price of the house with respect to the size of the house.


Backpropagation and Gradient Descent

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Backpropagation and gradient descent are two different methods that form a powerful combination in the learning process of neural networks. Let's try to understand the intuition of how this works. Neural networks learn through forward propagation, by using weights, biases, and nonlinear activation functions to calculate a prediction y from the input x that should match the true output y as closely as possible. There are several different loss functions and which one you choose depends on the type of machine learning problem you are facing. The goal of backpropagation is to adjust the weights and biases throughout the neural network based on the calculated cost so that the cost will be lower in the next iteration.


Implementing Gradient Descent in Python from Scratch

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A machine learning model may have several features, but some feature might have a higher impact on the output than others. For example, if a model is predicting apartment prices, the locality of the apartment might have a higher impact on the output than the number of floors the apartment building has. Hence, we come up with the concept of weights. Each feature is associated with a weight (a number) i.e. the higher the feature has an impact on the output, the larger the weight associated with it. But how do you decide what weight should be assigned to each feature?