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# Category: Other

### #005A Logistic Regression from scratch

Gradient Descent on m Training Examples In this post we will talk about applying gradient descent on $$m$$ training examples. Now the question is how we can define what gradient descent is? A gradient descent is an efficient optimization algorithm that attempts to find a global minimum of a function. It also enables a model to calculate the gradient or direction that the model should take to reduce errors (differences between actual $$y$$ and predicted $$\hat{y}$$).  Now…

### #004A Logistic Regression – The Computation Graph

Logistic Regression – the Computation Graph Why do we need a computation graph? To answer this question, we have to check how the computation for our neural network is organized. There are two important principles in neural network computation: Forward pass or forward propagation step Backward pass or backpropagation step During NN’s forward propagation step we compute the output of our neural network. In a binary classification case, our neural network output is defined by…
Logistic Regression – Cost Function Optimization First, to train parameters $$w$$ and $$b$$  of a logistic regression model we need to define a cost function. Given a training set of $$m$$ training examples, we want to find parameters $$w$$ and $$b$$, so that $$\hat{y}$$ is as close to $$y$$ (ground truth). Here, we will use $$(i)$$ superscript to index different training examples. Henceforth, we will use loss (error) function $$\mathcal{L}$$ to measure how well our algorithm is doing. The loss…
Binary Classification Binary classification is the task of classifying elements of a given set into two groups. Logistic regression is an algorithm for binary classification. Example of a binary classification problem: We have an input image $$x$$ and the output $$y$$ is a label to recognize the image. 1 means cat is on an image, 0 means that a non-cat object is in an image. In binary classification, our goal is to learn a classifier that can… 