(no title)
tdekken | 3 years ago
I seem to be in a similar situation as an experienced software engineer who has jumped into the deep end of ML. It seems most resources either abstract away too much detail or too little. For example, building a toy example that just calls gensim.word2vec doesn't help me transfer that knowledge to other use cases. Yet on the other extreme, most research papers are impenetrable walls of math that obscure the forest for the trees.
Thus far, I would also recommend Andrej Karpathy's Zero to Hero course (https://karpathy.ai/zero-to-hero.html). He assumes a high level of programming knowledge but demystifies the ML side.
--
P.S. If anyone is, by chance, interested in helping chip away at the literacy crisis (e.g., 40% of US 4th graders can't read even at a basic level), I would love to find a collaborator for evaluating the practical application of results from the ML fields of cognitive modeling and machine teaching. These seemingly simple ML models offer powerful insight into the neural basis for learning but are explained in the most obtuse ways.
ttul|3 years ago
I'm enrolled in their latest course via University of Queensland; presently, they're teaching us by implementing one of the latest text-to-image papers in PyTorch. They cover the math as side lectures if you're interested in it and have the pre-requisite knowledge. But it's not necessary if what you're keen on is the programming of models.
Heidaradar|3 years ago
also for your PS, can you give a little more detail? What's your end result, what have you done so far etc
tdekken|3 years ago
So far, I am a week into learning ML :). I have spent ~30 hours watching various ML courses and am in the process of testing the hypothesis that teaching reading with a shallower orthography (e.g., differentiating between the short and long 'e' sounds by introducing an 'ē' grapheme) leads to improved recognition of sublexical patterns. The step I am working on is building an embedding layer to ensure that these new graphemes (i.e., 'ē', 'ā', etc.) are near their parent grapheme (i.e., 'e', 'a') in the embedding space. (Although the model seems straightforward, I could also be completely misguided in how I am tackling this problem :) ).
FYI, this orthographic approach (i.e., how words are spelled using an alphabet) is used in a few highly researched literacy programs, but AFAICT there isn't direct research on the approach itself. The motivation is to initially make English a consistent language (i.e., the letters you see have a one-to-one correspondence with a particular sound). This should greatly simplify the initial roadblock in learning to read English (as seen by studies of countries with "shallow" orthographic languages) and then learners would transfer this knowledge to the normal (inconsistent) English orthography.
tdekken|3 years ago
My main goal is to use cognitive modeling to evaluate the efficacy of interventions and inform the personalized "minimum effective dose" for a particular learner. Academically, this is well-trodden territory [0-2] but these results haven't found there way into practice. This is critically important because we know that ~30% of children will learn to read regardless of method, ~50% require explicit, systematic instruction, ~15% require prolonged explicit and systematic instruction, and up to 6% have severe cognitive impairments that make acquiring reading skills extremely difficult [3]. Yet, how much is enough?
To make this more concrete, imagine you are learning a foreign language with Duolingo. How much effort per day is necessary to achieve that? Many people have long streaks and are no closer to fluency (I learned nearly nothing despite a 400 day streak). Similarly, many reading interventions are once-a-week and, predictably, don't meaningfully affect the learning outcomes for those students.
BTW, this ML portion is part of a much larger effort (e.g., our team is a Phase II finalist in the Learning Engineering Tools Competition). If anyone is interested in collaborating, please feel free to reach out to me.
[0] Phonology, reading acquisition, and dyslexia: insights from connectionist models (https://pubmed.ncbi.nlm.nih.gov/10467896/)
[1] Modeling the successes and failures of interventions for disabled readers. (https://www.researchgate.net/publication/243777699_Modeling_...)
[2] Learning to Read through Machine Teaching (https://arxiv.org/abs/2006.16470)
[3] Education Advisory Board. (2019). Narrowing the Third-grade Reading Gap: Embracing the Science of Reading, District Leadership Forum: Research briefing