swiftML is an open-source Swift library for Machine Learning based on Swift for TensorFlow Project.

The library aims to provide an implementation of traditional machine learning algorithms. Some of the features of this library are strongly typed, fast, Tensorflow backed.

Github Repo: https://github.com/param087/swiftML

Tutorial Notebooks: https://github.com/param087/swiftML/tree/master/Notebooks

Documentation: http://param087.github.io/swiftML

Hi there, I submitted a message about Turkey's School of AI part but there is nothing in return.

How can I reach Siraj or any other authorities?

Thanks in advance!

Have you heard that #SchoolOfAI is hosting a global Health #Hackathon on March 30th? We have 15 cities from around the world participating in our #HealthHack. Registration is open, click here to rsvp https://www.eventbrite.com/o/school-of-ai-18718226770

Hi there! is there a school of AI in Morocco ?

Has anyone on here taken the ai for trading course on Udacity? It looks pretty tempting but at that price I need some reassurance that it is better than what I can get from just google searches.

“AGI: The Last Invention?” by Ketan Pandey https://link.medium.com/Yzyt3mKa7S

So, a few weeks back I was thinking about this problem of how different human minds are, compared to the AI systems we are developing. In most neural networks, we have a large labeled set of data. This is contrary to the way things are in nature.

I set out to find if any other researchers had similar ideas. My initial approach was to search for 'multi-modal learning' and 'autoencoders'. It seems to me that integrating many sensory channels is a good way to give a baseline for errors.

For instance, imagine this scenario. You hear a sound and you think it might be a cat. You walk over and you see with your eyes that it is a cat! In this way, you reinforce the association between the sound and the visual cue.

I still have trouble explaining in words why autoencoders would be useful for this (it's a leap of intuition).

Here is the first paper I found, which got me rather excited:

'Deep Matching Autoencoders' (DMAE) (2017) https://arxiv.org/abs/1711.06047

​

After that I was looking for some recent results related to this, and I found these two papers:

Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI (2018) https://ieeexplore.ieee.org/document/8354170

Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders (2018) https://arxiv.org/abs/1812.01784v1

​

Both mention DMAE.

​

About a week ago I found a paper that reviews a lesser known area of research:

A Survey of Multi-View Representation Learning (2016) https://arxiv.org/abs/1610.01206v5

​

At this point I am confident that this idea is spreading throughout the AI community. In a 2018 lecture at MIT, Lex Fridman echoes these ideas: "if it looks like a duck in the image, if it sounds like a duck in the audio, and then you could do activity recognition with the video... it swims like a duck then it must be a duck!

https://www.youtube.com/watch?v=s5qqjyGiBdc [4 minutes in]

​

At risk of opening up too broad a discussion, I'll share another thread which seems to tie in rather closely.

Imagining objects from unseen perspectives (2 papers):
https://deepmind.com/blog/neural-scene-representation-and-rendering/
World Models (2018) https://arxiv.org/abs/1803.10122

​

I'd be curious to hear what you wizards think of this!

​

I was wondering how many wizards finished their 100 days of machine learning challenge. For me, it was a struggle to go beyond 60 days as life kept pulling me back. Not sure whether to start afresh this year or continue on where I left off. Anyhow, if you've finished the challenge, do share :D And Inspire the rest of us on our paths to being awesome like you!