We reached the end of our introductory journey in deep learning.

Now you understand what this is all about. Maybe you really like it and are ready to deepen your knowledge in the topic(deepen, deep learning, get it? Uhgg).

This will be a shorter article, I'll just offer some

In the previous article we learned how to use Keras to build more powerful neural networks. Professional-grade libraries like Keras, Tensorflow, and Pytorch let you build neural networks that can learn intricate patterns and solve novel problems.

Deep-learning networks lets learn subtle patterns thanks to their inherently large hypothesis space,

We already covered the most important deep learning concepts and created different implementations using vanilla Python. Now, we are in a position where we can start building something a bit more elaborate.

We'll use a more hands-on approach by building a deep learning model for classification using production-grade software.

You

In the previous article we learned that neural networks look for the correlation between the inputs and the outputs of a training set. We also learned that based on the pattern, the weights will have an overall tendency to increase or decrease until the network predicts all the values correctly.

In previous articles, we learned how neural networks adjust their weights to improve the accuracy of their predictions using techniques like gradient descent.

In this article, we will take a look at the learning process using a more abstract perspective. We will discuss the correlation between inputs and outputs in