I’m experimenting with transfering Pytorch/Keras/TF models to Fastai. My last post was about molecule generation using LSTM. Today I bring you a Message Passing Neural Net for bioactivity prediction!
I just published a two-part introductory article on building your own image classifier for deep learning beginners - if you’ve already completed Lessons 1-3 this is nothing new, but my target audience is myself before I started Deep Learning 2 years ago.
Its a hands-on article so it walks you through building a dataset and a model on Colab and running it on binder. If you have problems in the walk-through, I’d appreciate a comment so I can fix it…
Hi all, my name is Dino and I am new to fastai. I absolutely love the material and the teaching style. I’ve read the first 7 chapters in the book and listened to the lectures online. Before moving further into the book, I decided to jump into a project and I was able to create a multilabel classification model on the “chinese-mnist” dataset. I would appreciate any feedback and I am really excited about moving forward with this course.
I have written a couple of blog posts explaining the workings of FastAI Optimizers and FastAI training loop. I have read the source code for these and have demonstrated my understanding with an Image classification example. Find the link to my posts below. Hopes this will be helpful.
I continued to reflect my new knowledge in blog posts, so there is a new post about classifying digits from MNIST dataset from scratch. It was inspired by the chapter 4 of the fastbook, except this time we classify all 10 digits, and have some fun with different loss functions.
I recently started working through the fastbook, and created two posts covering chapters 4 and 5. Use the posts to supplement the videos, and please feel free to reach out if you have any feedback!