PyLessons March 25, 2019 Understanding Logistic Regression Sigmoid function Introduction to sigmoid logistic regression function that gives an ‘S’ shaped curve that can take any real-valued number and map it into a value between 0 and 1

PyLessons March 26, 2019 Reshaping arrays, normalizing rows and softmax function in machine learning In this tutorial, we'll learn how to reshape arrays, normalize rows, what is broadcasting, and softmax. All of this is extremely useful in machine learning

PyLessons March 27, 2019 Vectorized and non vectorized mathematical computations When we are writing machine learning functions, we must be sure that our code is computationally efficient so we always use vectorization

PyLessons April 01, 2019 Prepare logistic regression data with Neural Networks mindset Logistic regression is a binary classification method. In this full tutorial, we will start writing an algorithm that could predict the correct animal in a given picture

PyLessons April 03, 2019 Logistic Regressions architecture of the learning rate In this part, we'll build a Logistic Regression using a Neural Network mindset. We'll see that it is actually a straightforward Neural Network model

PyLessons April 04, 2019 Logistic Regression cost optimization function In this tutorial, we will learn how to update learning parameters (gradient descent). We'll use parameters from the forward and backward propagation

PyLessons April 05, 2019 Logistic Regression predict function In this tutorial, we will implement the predict() function and we will be able to use w and b to predict the labels for our dataset X

PyLessons April 08, 2019 Final cats vs dogs logistic regression model In this tutorial, you will see how the overall model is structured by putting together all the building blocks together in the right order

PyLessons April 09, 2019 Best choice of learning rate in Logistic Regression In order for Gradient Descent to work, we must choose the learning rate wisely. In this part, you will see how the learning rate determines how rapidly we update the parameters