In the last article, we learned many different ways in which we can create ndarrays. Now that we know how to create NumPy arrays it's time to start playing around with them.
We will learn to perform basic operations, a task you can divide into 3 categories:
NumPy (an acronym for Numeric Python) is a library for working with multi-dimensional arrays and matrices. It was created in 2005 by Travis Oliphant, and since then received numerous contributions from the community that enabled it to grow into one of the most used tools in data science.
NumPy lets
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,
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.