Although Linear Least Squares Regression is simple and precise, it can be inefficient when matrices get very large. Cholesky decomposition is another approach to solve matrices efficiently by Linear Least Squares, as it decomposes a matrix into a lower and upper triangular matrix (L and LT). Finally, linear regression with Cholesky decomposition...

Continue reading...# Software Development

## Linear Least Squares Regression withÂ TensorFlow

Linear Least Squares Regression is by far the most widely used regression method, and it is suitable for most cases when data behavior is linear. By definition, a line is defined by the following equation: For all data points (xi, yi) we have to minimize the sum of the squared...

Continue reading...## Classification Loss Functions (PartÂ II)

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

Continue reading...## Loss Functions (PartÂ 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...

Continue reading...## 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...

Continue reading...## Working with Matrices inÂ 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. Letâ€™s define the matrices we are...

Continue reading...## Understanding Variables and Placeholders inÂ TensorFlow

Usually, when we start using TensorFlow, itâ€™s very common to think that defining variables is just as trivial as a HelloWorld program, but understanding how variables (and placeholders) work under the hood is very important to understand more complex concepts because those concepts heavily use variables/placeholders; and, if we donâ€™t...

Continue reading...## Declaring tensors inÂ TensorFlow

[Requirement: Tensorflow and NumPy installed on Python +3.5][Requirement: import tensorflow as tf][Requirement: import numpy as np] Tensors are the primary data structure we use in TensorFlow, and, as Wikipedia describes them, â€śtensors are geometric objects that describe linear relations between geometric vectors, scalars and other tensorsâ€ť. Tensors can be described...

Continue reading...## Cross-Platform Communications: gRPC Server and Client:Â Node.js

This is my second video on gRPC, this time I was experiencing Node.js to make an app communicate with aÂ gRPC .NET CoreÂ app. Here the details about Node.js implementation:

Continue reading...## Cross-Platform Communications: gRPC Server and Client: .NETÂ Core

When we are developing web solutions consisting of several projects, it is very common communicate between each other, and the common solutions are proprietary solutions (like .NET Remoting), or standard solutions like REST or SOAP. Recently, I discovered gRPC as the Google solution for Cross-Platform communications, allowing the developers to communicate...

Continue reading...