105: Backpropagation – A Deeper Dive

Hi Readers!!!

After the last post I had received feedback that I could go a bit more deeper into the concept of backpropagation. So in this post I am going to explain in detail the underlying calculus of backprop and also illustrate one epoch with an example.

104: The one with the “Learning”

Hi readers!

In the past two post we saw the different parameters and functions used in a NN. In this post, we are going to see about the very last topic, Backpropagation.

So let’s dive in!

103: The One with Errors and Costs

Hey Readers!

In the last post we saw the neuron getting activated. That is an important step in forward propagation. In this post we will see about the different error functions and cost functions that give the feedback for a network. In other words they are important in back-propagation (backprop).

102: The One With the Activation

Hello Readers!

In the next 3 posts, we are going to see some concepts that are fundamental for ML and DL.

The Neuron

It is the building block of any neural network. It has been modelled based on the actual biological neuron.

I am not going to dive deep into the biological concepts of the nervous system. If you want to learn more about it refer here.

We saw the basic structure of a neuron.

Deep Learning 101: The One Where It All Begins

Hi readers!

In this post, we will see the history of DL. This will help us to know about the effort that has gone into developing AI to the level it is today.