Hello everybody,

As two under grad students we just started in the field of machine learning, and made a toy project to break simple captchas.

​

Although it is a modest project, we wanted to share our experience. We would like to hear your opinions and suggestions on it.

​

Here is the write-up for the project :

https://cagriuysal.github.io/Simple-Captcha-Breaker/

​

Thank you all.

https://youtu.be/7MKcoctU-es

Speech synthesized with my own Trump TTS model.

Hi guys,

​

Just tried to experiment with GPT-2 for hotel reviews generation -> https://github.com/tomasrasymas/gpt2-hotel-reviews

After analysing the results, I think GPT-2 is not suitable for such a task (short text, reviews generation), cause reviews in dataset are very similar and talking about same thinks, there are no context for model to learn. In my opinion using simpler model like few LSTM + Attention might produce same or even better results.

​

What do you think?

Hey all, I recently stumbled upon a great research paper that puts together lessons learned by machine learning researchers and practitioners for developing successful ML applications.

I’ve summarized this “folk wisdom”, that is often hard to come by but is much needed, here: https://towardsml.com/2019/04/09/12-key-lessons-from-ml-researchers-and-practitioners/

Check it out. Hope it helps! 🙂

I’ve finished working on the new algorithm which is based on ID3; entropy and information gain. Interested to see feedback! Challenge Incredicat!

——-

Wasn’t sure where to post this as I’m still working on it but wanted to put it out there to get any useful feedback or thoughts from the experts. It’s basically a game similar to 20 Questions (or Animal, Vegetable, Mineral) that attempts to ask you questions to work out an object you are thinking about. You can think of everyday items (animals, household objects, food, quite a bit of other stuff etc) and it has 30 questions to try and guess the item. I’ve been working on it for a while but not sure what to do next so interested to hear anyone’s thoughts…

The link for anyone that wants to try it out is incredicat.com

Thanks in advance!

I have the opportunity to give a 45-min ML tutorial to a non-technical audience, and this is what I’m planning. I’d really appreciate any feedback / comments. Thanks!

Part 1: What is a neural network? (15 mins) – I’ll either show this video or give a similar walkthrough

Part 2: Build your own neural network in 15 minutes! (15 mins) – I’m going to create a Jupyter notebook on Google Colab, using Keras to build a model for MNIST in as few lines of code as possible. – During the session, I will share the Colab link with the audience and we’ll walk through the steps together. – Question: Is there any way to incorporate some interactive image drawing capability into Colab such that the audience can draw a digit with their mouse and get a model prediction?

Part 3: Demo some other cool image recognition models (5 – 10 mins) – The goal here is to demonstrate the power of some readily available models such as Imagenet, and the fact that I can easily run these models in minutes. I will ask the audience to share images with me and run them through the model. – I’ll close with a brief discussion of how easy it is to get these models into production, enabling users to build their own apps, etc.

Part 4: Q&A (5 – 10 mins)

Consider an online financial advisor (robo-advisor) that makes investment decisions tailored to the risk-preferences of a customer. As time goes by, the customer’s risk-preferences change, and when the uncertainty is high enough, the machine become unsure about what investment decision to make, and pings the customer to ask for updated information.

Can this be viewed as an active learning situation?

In active learning one strategically decides which samples to label, based on the information it provides. Here there is really just one “data point”, the customer, that is “labeled” to begin with. The active learning analogy would be that when the machine becomes unsure about the label (risk-preferences), it asks for re-labeling (updated risk-preferences), i.e., it re-labels at a point where it provides a lot of information.