The Machine Learning Blog
This is the second article in a series of three articles, explaining how to build an image classifier without coding. If you have not read part 1, please read it here before continuing.
I am a biologist and from my precarious corner of knowledge, but thanks to the close relationship with professionals from multiple disciplines, mostly linked to the Neuroscience Institute of the Universidad del Bosque, I have entered into the fascinating adventure of understanding the basics of Artificial Intelligence.
How to train a Machine to recognize handwritten numbers in a picture (or anything else for that matter)
Recognizing hand written numbers
In this post, I will explain how to use Machine Learning to build a piece of software that is able to recognize handwritten numbers. I will also explain what other applications this could have for you and your business. This being said, I will not go into implementation details, nor into the mathematics of how this works, the post will just explain the general idea behind this principle.
Neural Networks are at the core of Artificial Intelligence. They are used by organisations around the world to innovate, automate tasks and to become more competitive. In this article I will explain in plain terms and without any mathematics or technical terms what they are and how you can use them to innovate in your domain.
Machine Learning used to be really expensive
Once upon a time, if you wanted to implement a machine learning project, besides from an exploitable dataset, you also needed two things:
- An incredibly expensive salary (over $300K) to hire a ML expert, specialised in a particular field (deep learning, convolutional neural networks, etc)
- Huge and very expensive IT infrastructure, specially to do the parallel computing required to train ML models.
Both these elements made it impossible for normal companies to implement Machine Learning in their every day operations and ML was reserved to only the wealthiest corporations in the world.