Making machine learning models work in the wild, and what I learned along the way.
In the last third of the lesson 2 video, Jeremy goes into detail about linear regression and stochastic gradient descent. I found this part of the lesson really helpful, so I took some in depth notes!
This week in the fast.ai course we got more into the details of getting data for image classification models, playing around with the different training parameters, and running them on sample data. This blog post is focused on some of the main code steps in the [lesson 2 Jupyter notebook](https://github.com/fastai/course-v3/blob/master/nbs/dl1/lesson2-download.ipynb), and how I used it to build my own penguin image classifier!
RC's community cluster is a great resource to take advantage of! I had a rough time getting the Fast AI course set up, so I wrote down some instructions (hopefully) help others save some time.
During my time at the Recurse Center, I’m taking a machine learning course via Jeremy Howard’s Fast.ai - “Practical Deep Learning for Coders.” What attracted me to this course over others (like Andrew Ng’s Coursera course) was the top-down learning approach: the structure is hands on building models first, and diving into the theory later.