Building useful machine learning tools keeps getting easier: A fish ID case study
Lessons learned building a fish ID web app with fast.ai and Streamlit, in an attempt to reduce my fear of missing out on the latest deep learning developments.
deep learning
Use your human brain to avoid artificial intelligence disasters
Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.
The rise of greedy robots
Given the impressive advancement of machine intelligence in recent years, many people have been speculating on what the future holds when it comes to the power and roles of robots in our society. Some have even called for regulation of machine intelligence before it’s too late. My take on this issue is that there is no need to speculate – machine intelligence is already here, with greedy robots already dominating our lives....
Why you should stop worrying about deep learning and deepen your understanding of causality instead
Everywhere you go these days, you hear about deep learning’s impressive advancements. New deep learning libraries, tools, and products get announced on a regular basis, making the average data scientist feel like they’re missing out if they don’t hop on the deep learning bandwagon. However, as Kamil Bartocha put it in his post The Inconvenient Truth About Data Science, 95% of tasks do not require deep learning. This is obviously a made up number, but it’s probably an accurate representation of the everyday reality of many data scientists....
The joys of offline data collection
Many modern data scientists don’t get to experience data collection in the offline world. Recently, I spent a month sailing down the northern Great Barrier Reef, collecting data for the Reef Life Survey project. In addition to being a great diving experience, the trip helped me obtain general insights on data collection and machine learning, which are shared in this article. The Reef Life Survey project Reef Life Survey (RLS) is a citizen scientist project, led by a team from the University of Tasmania....
Learning about deep learning through album cover classification
In the past month, I’ve spent some time on my album cover classification project. The goal of this project is for me to learn about deep learning by working on an actual problem. This post covers my progress so far, highlighting lessons that would be useful to others who are getting started with deep learning. Initial steps summary The following points were discussed in detail in the previous post on this project....
Hopping on the deep learning bandwagon
I’ve been meaning to get into deep learning for the last few years. Now, the stars having finally aligned and I have the time and motivation to work on a small project that will hopefully improve my understanding of the field. This is the first in a series of posts that will document my progress on this project. As mentioned in a previous post on getting started as a data scientist, I believe that the best way of becoming proficient at solving data science problems is by getting your hands dirty....