Background on Myself
On August 30th, 2018, I graduated from the Udacity Deep Learning Nanodegree. Unlike many others to go through the program, I do not have a Computer Science or Engineering degree. In fact, I don’t have any graduate or undergraduate degree. My highest form of a completed college education is an Associate of Science in Electronics Technology from a community college, which basically went into basic small electronic theory like modulation, simple circuits, and circuit board components. My professional background is in Network Engineering and Cybersecurity.
Going into a field of study like machine learning with no exposure to statistics, calculus, linear algebra, and whatever little programming experience I got from Codecademy was a challenge. And I nailed it through long nights, lots of research, and tons of self-doubt. If you are passionate about doing something new, I believe you can find ways to make it happen!
This isn’t a review of the Nanodegree, but it is amazing and highly recommend for anyone wanting to learn the basics of deep learning. This is about what my life is like after completing it.
Suddenly, I found myself with all these new skills and tools at my disposal, and every problem began to look like something that had to be addressed with some kind of neural network architecture.
But the same dilemma that has stalled many would-be programmers, reared its ugly head….what to do? I found I didn’t have the domain expertise for all the problems I wanted to tackle, things like self a flying drone, stock market prediction, a dishwashing robot, or facial recognition for my home. I knew that I could crack any of these projects, but starting from scratch is daunting. The issue was finding a subject I knew intimately and applying my new skills to further my proficiencies.
Cue the idea lightbulb!
Machine Learning enhanced Cybersecurity was my calling.
The Future is Bright
In future posts, I’ll be documenting my contributions to the open-source cybersecurity community. I have already begun building and testing open frameworks for Network and Host-based Intrusion Detection using LSTMs and plan on advancing free anti-viruses with GAN inspired detection methods, and Deep Reinforcement Learning based active defense agents.