In this tutorial, we will see how to write code to run a neural network model that can be used for regression or classification problems.
We will NOT use fancy libraries like Keras, Pytorch or Tensorflow. Instead the neural network will be implemented using only numpy for numerical computation and scipy for the training process.

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Hello! In this post I will teach you how to do a simple data classification using the KMeans algorithm. We will go through the concept of Kmeans first, and then dive into the Python code used to perform the classification.

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Welcome to one more tutorial! In the last post (see here) we saw how to do a linear regression on Python using barely no library but native functions (except for visualization).

In this exercise, we will see how to implement a linear regression with multiple inputs using Numpy. We will also use the Gradient Descent algorithm to train our model.

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Among the variety of models available in Machine Learning, most people will agree that Linear Regression is the most basic and simple one. However, this model incorporates almost all of the basic concepts that are required to understand Machine Learning modelling.

In this example, I will show how it is relatively simple to implement an univariate (one input, one output) linear regression model.

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A car is moving at 100 miles per hours. How many minutes it takes to go from A to B, distant 345 kilometers?

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Hello!

In the last post I demonstrated how to use MS Excel software and its Solver tool to find the optimal operating point in a process.

In this post, we will use the free Python programming language to solve the same problem. The use of Python in the academic and industrial environment has grown immensely, mainly due to the continuous development of new tools that increase its potential of application in several areas.

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A common problem which many engineers face on their jobs is process optimization. How to maximize a profit or how to minimize costs of a determined process so as to make it the most profitable possible? There is a diversity of mathematical techniques which can be used to solve such problem. Most of these techniques differ in its applicability when a problem is linear or non-linear.

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Hello and welcome! This is the first post of the blog Fundamentals of Engineering.

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