AI & Machine Learning

In this post, you will learn about the mathematical objects of Linear Algebra that are used in Machine Learning. You will also learn how to multiply, divide, add and subtract these mathematical objects.  You will also learn about the most important properties of Matrices and why they enable us to make more efficient computations. On top of that, you will learn what inverse- and transpose Matrices are and what you can do with it. Although there are also other parts of Linear Algebra used in Machine Learning, this post gives you a proper introduction to the most important concepts.

Machine learning methods are used in a wide range of areas. In this post, I will discuss a few of the ways that machine learning is used within the education space – specifically K-12. Machine learning has the potential of making a significant impact in this space, so let’s look at some of those areas. 

In terms of cyber security AI is as much a tool for the implementation of security measures, and the vendors selling them, as it is for cyber attackers. Every time a new network security technology is introduced, you can be certain terms such as deep learning, machine learning, self-learning algorithms, cognitive analytics and neural networks are part of the pitch.  But the industry’s focus on AI as a way to boost protection against threats ignores the larger problem, which is that of ‘self-learning attackers’.

Faster R-CNN, R-FCN, and SSD are three of the best and most widely used object detection models out there right now. Other popular models tend to be fairly similar to these three, all relying on deep CNN’s (read: ResNet, Inception, etc.) to do the initial heavy lifting and largely following the same proposal/classification pipeline.  At this point, putting these models to use just requires knowing Tensorflow’s API. Tensorflow has a starter tutorial on using these models. Give it a try!