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

# Cholesky Decomposition for Linear Regression with TensorFlow

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

# Linear Least Squares Regression with TensorFlow

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

# 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…

# Loss Functions (Part 1)

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

# Activation Functions in TensorFlow

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

# 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…

# Understanding Variables and Placeholders in TensorFlow

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

# Declaring tensors in TensorFlow

[Requirement: Tensorflow and NumPy installed on Python +3.5] [Requirement: import tensorflow as tf] [Requirement: import numpy as np] Tensors are…

# Java Streams API in brief

First, let’s define what a stream is in Java 8: a sequence of functions, actions, inputs, and outputs (better defined…