Focus on data transformation and scenarios, not math, if you want to get into real-world Machine Learning. Yes, you’ll eventually have to learn some math, but the abstraction level within ML engineering has already moved on.
Learning the theory and math at the foundations of Machine Learning is like …
more ...Since last Fall, I’ve been working on reidentifying reef manta rays with “low k-shot” (few labeled examples). That project is now deployed to the scientists and, until they start giving me feedback (and hopefully, more labeled data) pretty much at the finish line.
My …
more ...The best orientation to machine learning (ML) I could find is Machine Learning Explained from MIT Sloan. That gives a good overall orientation, even if it, like all texts, suffers from underestimating the speed at which ML capabilities are evolving. I don’t think …
more ...Although I have no domain experience with satellite imagery, I've used convolutional neural nets with aerial photography to recognize marine debris. So when I saw the DataDriven challenge 'Tick Tick Bloom' I took a glance at the dataset …
more ...The Mastodon API is very straightforward, as is the OpenAI API for its NLP models. I wrote a quick proof-of-concept program to do sentiment analysis of "toots.".
more ...My current project is re-identifying individual manta rays (Mobula alfredi and Mobula birostris) by their distinct belly patterns … er… ventral markings.
Every night at two spots on the Big Island of Hawai’i where I live, dive boats shine bright lights that attract plankton. Most nights, the plankton in turn …
more ...The "attention" in ML is "what you should attend to," not "alertness." In the sentence "They crossed the to get to the other bank." you need to "attend to" the word to disambiguate "bank". If is "street" then it's "bank" as in "financial institution" (most likely). If is "river" then …
more ...If you have a large FastAI (v2) DataLoaders and you're trying to debug something at epoch-scale (such as a custom metric), an easy way to train on a small subset of your data is:
subset_size = 100 # Or whatever
selected_items = np.random.choice(dls.train_ds.items, subset_size, replace=False)
# Swap in …The Chinese Room is a 1980 thought experiment from the philosopher John Searle. The Wikipedia summarizes the setup:
more ...[S]uppose that artificial intelligence research has succeeded in constructing a computer that behaves as if it understands Chinese. It takes Chinese characters as input and, by following the instructions of a …
I just deleted my intermediate data and models for Kaggle’s Happywhale competition. I did terrible, never getting much above pure random guessing. Which was frustrating, because it’s a problem in Machine Learning that I’m very interested in (and want to do more work in).