Project Perseus

  What did I Learn this Week? This week in my educational journey in machine learning, I learned how to track model metrics, such as accuracy and precision, I learned how another kind of model works, called binary classification , and finally I learned some Tensorflow basics. Tensorflow is a really neat library created for the python programming language that lets you easily set up and train models using its Keras API . I started going through some videos that taught me the basics of how to import data, compile the model, fit the model, and evaluate the model's effectiveness.  So, I've boiled down everything that I've learned in the past couple weeks into a post that briefly describes the ML concepts I went through and the code I used to practice those concepts. Let's get started! Basics of a Binary Classification Model A binary classification model is one that seeks to categorize data points as one option out of two possible options. For example, it could be used to ...

  Continuing to Optimize Dynamic Light Scattering Signal In the last post I made detailing my time as an undergraduate researcher at the University of Mississippi, I described some of the difficulties I was facing in regards to the Dynamic Light Scattering (DLS) instrument.  Well, the drama and the trauma only increased this week, but needless to say, I've learned a lot more about DLS than I would have if everything had worked out perfectly the first time.  So what happened this week exactly? Reducing the Signal-to-Noise Ratio Pretty much the name of the game this week was to limit the noise that the DLS machine was picking up on. There were a lot of things that went into this and first and foremost I'll start with what I left off with last time: trying a new sample well plate with opaque walls instead of clear ones. My thought was that this would reduce the high intensities and the highly variable signals that I was getting from just de-ionized water and buffer alone. So...

  Learning and Experimenting with Dynamic Light Scattering This past week at my Ole Miss REU research program has been...interesting to say the least. Not in a bad way, just in a I'm-going-insane-because-all-my-data-is-wacky-no-matter-what-I-do kind of way. The purpose of these experiments that I've been trying to do involves gaining data about the size and distribution of DNA/mercury complexes in solution. I outlined in my first post of this series how Dr. Wadkins has demonstrated that mercury complexes with DNA and induces it to form secondary structures. It's also postulated that these structures can be thought of as fully-fledged nanoparticles.  Dynamic Light Scattering (DLS) is a common way to obtain data about nanoparticles. Therefore, getting usable data from the DLS instrument from my DNA/mercury samples was my first order of business in order to develop an idea of how increased mercury concentrations correlates with particle size in solution. I thought it would be ...

  Project Perseus AI ~ Post #2 ~ Digging Deeper Into how Machines Learn This week I built off of what I learned from last week! I didn't take giant leaps in stretching my horizons of the subject, but rather, I really fleshed out some more basic principles (something I could probably do forever), but at some point I would have to move on. However, what exactly did I learn about this week? What I Learned this Week I primarily learned about what an activation function is, and what the different types of activations there are. I also learned about logistic regression , which is a logical "next step" to linear regression which I talked about in Post #1. In a way, last week I learned how machines learn, this week I learned about how they think which their learning is a result from. So I just went a little bit deeper into the mechanics of things. Okay, let's start off with the meat of the material: Logistic Regression Many times, in machine learning contexts, you want the sy...

Working with Acutely Toxic Chemicals is Always a Fun Time... The first part of the drama that has been unfolding with my research with Dr. Wadkins has been due to a nasty little chemical called mercury(II) perchlorate trihydrate. This inorganic mercury salt is going to be our source of mercury ions in solution for when we titrate it with the DNA solution to observe the effects of heavy metal ion intercalation of DNA base pairs and subsequent secondary structure formation. Of course everything in life is hard, right? And nothing ever goes to plan. At least I think that's a safe motto to live by especially in scientific research. So what was going wrong? Mercury(II) Perchlorate is Hygroscopic What does it mean to be hygroscopic? It essentially means that it absorbs water like crazy! When we opened the bottle of the mercury salt, we discovered there was a thick aqueous layer of water on top of the actual salt and that the salt was nearly cemented to the bottom of the bottle. I tried m...

  Project Perseus AI ~ Post #1 ~ Machine Learning Origins This is the first post in hopefully a series of many that will document my educational journey through the topics of AI and machine learning. Every post will discuss a topic that I've reviewed and/or models that I've programmed to help me better understand these machine learning concepts.  What is the goal with these posts? Again, the goal is to primarily give a record of my self-taught journey in machine learning and to one day create my own AI model which I will name Perseus. I'm quite far away from that point at present, but if I'm diligent enough, I believe I can get there.  I started this journey because I've recognized that the 21st century will most likely be defined as the advent of organic and artificial intelligence cooperation. I believe that a healthy and beneficial cooperation is possible, but that it will require deep understanding on the humans' part and (hopefully) from the AI as well. My ...