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

# Category: Machine Learning

# 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 … More

# 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 … More

# 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 … More

# Loss Functions (Part 1)

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

# Activation Functions in TensorFlow

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

# 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 … More

# 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 … More

# 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 … More