TensorFlow has several high-level libraries allowing us to reduce time modeling all with core code. TF Estimator makes it simple … More
Category: TensorFlow
TensorFlow Way for Linear Regression
In my two previous posts, we saw how we can perform Linear Regression using TensorFlow, but I’ve used Linear Least … More
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
