## Support Vector Machines (SVM) for Classification

The purpose of this document is to present the linear classification algorithm SVM. The development of this concept has been

Skip to content
# Category: Machine Learning

## Support Vector Machines (SVM) for Classification

## TensorFlow High-Level Libraries: TF Estimator

## TensorFlow Way for Linear Regression

## Cholesky Decomposition for Linear Regression with TensorFlow

## Linear Least Squares Regression with TensorFlow

## Classification Loss Functions (Part II)

## Loss Functions (Part 1)

## Activation Functions in TensorFlow

## Working with Matrices in TensorFlow

## Understanding Variables and Placeholders in TensorFlow

software developer & machine learning engineer

The purpose of this document is to present the linear classification algorithm SVM. The development of this concept has been

TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple

In my two previous posts, we saw how we can perform Linear Regression using TensorFlow, but I’ve used Linear Least

Although Linear Least Squares Regression is simple and precise, it can be inefficient when matrices get very large. Cholesky decomposition

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases

In my previous post, I mentioned 3 loss functions, which are mostly intended to be used in Regression models. This

Implementing Loss Functions is very important to machine learning algorithms because we can measure the error from the predicted outputs

Perceptron is a simple algorithm which, given an input vector x of m values (x1, x2, …, xm), outputs either

Matrices are the basic elements we use to interchange data through computational graphs. In general terms, a tensor can de

Usually, when we start using TensorFlow, it’s very common to think that defining variables is just as trivial as a