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Language Identification Project

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I wanted to complete some practice problems and build a small basic portfolio of small tech projects that showcase some of my recent learning at the University of Edinburgh. I have taken several courses about natural language processing (NLP) at uni.  NLP is a very wide term for a variety of different tasks, for example, text classification, named entity recognition, machine translation, language modeling, etc. This project was just a simple text classification task. I wanted to input text and have a model predict which language it is. Find the code on my  Github , and my steps that I took to create the project below. Hope this helps facilitate your learning too. Step 1: Find data Foundational to any project involving any sort of prediction. You need data and preferably a lot of it. I wanted to predict language, and one corpus of language data that I already knew about from school was the Europarl Dataset . This is a corpus of EU meetings that are translated into the languages of the E
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Free PDF Reader on Mac

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I was talking to my wife earlier this year about all the reading that we have to do in school. We have books, articles, journals, and tother pdfs that we need to read for our classes. Victoria had a lot in just her employment law class. I started looking into PDF Readers, it isn't a hard task to have a simple extract text from a PDF and then read it. It seemed to me that a very basic version of this could easily be implemented on our computer.  I started looking into open-source TTS models and looking at all types of technology. Then, I realized that I need to iterate. What is the bare minimum that I need to do to get something working. Once I get there, I can figure out what can be done to improve the system. I have used the Mac shortcuts and the Automator a few times before to define certain tasks on my local macbook. I figured that I could start there. Turns out there are built in functions that already allow me to access the native TTS system, so I built a Quick Action to read
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2023 Reflections

Looking back on my writings from this time last year, made me realize again how many twists and turns that happened to me this year. Let me walk you through it. As of the start of the year, the acquisition of Emmersion was complete and we officially became IXL Learning employees. I was starting to feel like a valuable team member. I had genuine friends at work. I was also getting very interested in our product. I learned a lot about elicited imitation and tried to start self learning about NLP. Because of this, I started exploring the idea turning my career towards the world of language technology. I was very interested in the ASR products that we used as a company. I helped lead an initiative about understanding the error rates of our current solutions and a few other state-of-the-art solutions on the market. I also wanted to start understanding more about using open response to inform our language proficiency score. I started conversations at work with other departments at Rosetta St
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Machine Learning Math Class

During fall semester at University of Edinburgh, a few of the required classes covered subjects that I already felt comfortable in (python, stats, and applied ML). I had the option to switch for a more challenging class. Luckily, I had another in my program that wanted to take Machine Learning and Pattern Recognition. It was designed by the university to prepare people for PhD research in machine learning, aka, a lot of math. I wish I had more appreciation and knowledge about linear algebra when I was taking MATH313 for my math minor at BYU. Linear algebra is so powerful and can be abstract and unintuitive at times. I wanted to write about some cool things that I learned during the class and outline my notes in a way that can be meaningful and potentially helpful. Week 1 in MLPR Feature engineering is important for ML tasks, but one goal of ML is to get rid of feature engineering and replace it with feature learning. $$\text{ML}=\text{fitting functions to data}$$ The simple function \(
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Why University of Edinburgh

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I loved my time as a software engineer at Emmersion/Rosetta Stone. I was super happy about working there because I was super interested in the core technology of the product that we were developing. I loved it when we were creating or maintaining the actual tests. I was very interested and pushing to be a part of the assessments team that would have responsibility over the quality of the tests. I liked being able to answer product questions with data, and I found myself interested in things that maybe fall outside of the role of an engineer in a larger company. I had lots of conversation with my good friend at work, who was the head of assessment, about how we could improve out data pipelines, as well as improve our tests. We were a small lean team, so there wasn't as much opportunity to work on test development as I would have wanted. However, I was able to do some experimentation with different ASR providers and also brainstorming about potential ML solutions for our open respons
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2022 Reflections

Purpose is critical for the success of any endeavor. I wanted to start this blog with a purpose statement. This purpose may change over time, however, if a purpose doesn't change it probably means you've made it or you are stubborn. I certainly haven't made it. My first purpose in having this blog is to become a better thinker through writing about my reflections, hopes, and learnings. In my experience, it is hard to be a good thinker, that is, to have things well thought out. I have also noticed the strong connection between writing and thinking. One who thinks well, can write well. One who writes well, can think well. The two go hand in hand. This year has been mainly about learning for me. I graduated from college and started my career. I have learned so much from the people around me about programming, working in teams, and relationships. I have tried to reflect on my experiences in this last year and try to understand what I need to change to improve myself. I want to
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