I recently wrote about, how to use a ‘imagenet’ pretrained efficientNet implementation from keras to create a SOTA image classifier on custom data, in this case the stanford car dataset. There are some details about BatchNormalization and how to start by training only the classifier layer and later train the complete network. But in the end it’s a good starter for beginners (using the jupyter notebook).

http://digital-thinking.de/keras-transfer-learning-for-image-classification-with-effificientnet/

If you are looking for a PyTorch example (not mine):

https://github.com/morganmcg1/stanford-cars

I’ve been wondering about this question:

• On one hand, conventional wisdom has that winning a Kaggle competition is quite a feather in your cap and it will open all sorts of doors for you. You will have to fend off recruiters with bear spray, given the amount of corporate attention you will receive once you win.
• On the other the few Kaggle winners that I follow personally (connecting on LinkedIn, following their blogs, etc…) don’t seem to have their careers impacted by their achievements. You don’t see them switching to Google or FB or something a few months after they win. They all stay in the relatively obscure tier 2 role they worked in. Sometimes not even that, they turn out to be freelancers and they remain that way, or something like that….

Any thoughts on what is the more accurate depiction of Kaggle winners?

An introduction / crash course about TensorFlow 2.0, as a colab notebook.

I’m involved in evaluating candidates for positions in ML and have been asked how to test their practical skills. We are looking at both potential ML engineers and research scientists. The positions are quite junior, so project management is not something we need to look for. We have a quite broad set of applications going on spanning computer vision, NLP, time series analysis and tabular data.

What I would like to do is to formulate a task (or tasks) that I can use to test the applicants’ practical problem solving abilities. My problem is that the tasks I work with involve a bit sensitive data that I can’t share. Open datasets on the other hand are often already formatted in a “ready-to-model” format with plenty of publicly available solutions, which is usually not the case in real-life projects.

I would like to discuss options that can also serve as a resource for struggling research scientists involved in recruiting. How would you formulate a task that:

• Is solvable within reasonable time before a job interview
• Is not more hardware demanding than that you can solve it in a Collab notebook
• Is not trivially solvable by reading online tutorials
• Shows that the applicant avoid some common pitfall encountered in practice (data leakage, imbalanced datasets, test set peeking)
• Shows that the applicant actually has some practical know-how?

Thought it was a joke, but it looks real….

https://www.sciencedirect.com/science/article/pii/S0957417419307079

Only Pretraining available, downstream tasks are not yet.

https://github.com/graykode/ALBERT-Pytorch