Blog Post

Neural Network Architecture in Machine Learning

In the previous lesson, we understood how logistic regression works and how we can implement it.

logistic regression example-pic-1
In the previous lesson, we used logistic regression for the binary classification of dogs based on previously seen and learned data

In this lesson, we will learn about the neural network architecture in machine learning and understand the building blocks of a neural network, be introduced to different activation functions, understand the difference between shallow and deep neural networks, and become ready to use our software tools to develop a neural network.

neural network architecture machine learning
This lesson will teach us about neural network architecture in Machine Learning (ML).

Watch the video version of the 7th lesson:

If you enjoyed this post, please consider contributing to help us with our mission to make robotics and mechatronics available for everyone. We deeply thank you for your generous contribution!

Do not forget to contact us:

Be sure to let us know your thoughts and questions about this post, as well as the other posts on the website. You can either contact us through the “Contact” tab on the website or email us at support[at]

Send us your work/ research on Robotics and Mechatronics to have a chance to get featured in Mecharithm’s Robotics News/ Learning:

Follow Mecharithm on the following social media too:

YouTube, and Instagram

Related Posts