# Python

## Classification Loss Functions (Part II)

In my previous post, I mentioned 3 loss functions, which are mostly intended to be used in Regression models. This time, I’m going to talk about Classification Loss Functions, which are going to be used to evaluate loss when predicting categorical outcomes. Let’s consider the following vector to help us...

## Loss Functions (Part 1)

Implementing Loss Functions is very important to machine learning algorithms because we can measure the error from the predicted outputs to the target values. Algorithms get optimized by evaluating outcomes depending on a specified loss function, and TensorFlow works in this way as well. We can think on Loss Functions...

## Activation Functions (updated)

Table of Contents What is an activation function? Activation Functions Sigmoid ReLU (Rectified Linear Unit) ReLU6 Hyperbolic Tangent ELU (Exponential Linear Unit) Softmax Softplus Softsign Swish Sinc Leaky ReLU Mish GELU (Gaussian Error Linear Unit) SELU (Scaled Exponential Linear Unit) What is an activation function? An activation function is a...

## Working with Matrices in TensorFlow

Matrices are the basic elements we use to interchange data through computational graphs. In general terms, a tensor can de defined as a matrix, so you can refer to Declaring tensors in TensorFlow in order to see the options you have to create matrices. Let’s define the matrices we are...