Piero is now at Stanford :). Piero is definitely worth listening to. He's been in the weeds, knows the details and yet keeps the bigger picture in mind. Also one of the nicest people around.
I took this class and can vouch for it. They update the class every year to go over recent research - not an easy task in such a fast moving field. For example, this offering covers the Transformer architecture which has recently been used to obtain state of the art results across a wide range of NLP tasks.
Since we're on the topic of tutorials to understand neural nets and modern deep learning, I will throw in Michael Nielsen's excellently written free online "book" on neural nets. It's really a set of 6 long posts that gets you from 0 to understanding all of the fundamentals with almost no prerequisite math needed.
Using clear and easy to understand language, Michael explains neural nets, the backprop algorithm, challenges in training these models, some commonly used modern building blocks and more:
This book opened my eyes to the power of textbooks written in such easy to understand, clear style. Bet it took repeated revisions, incorporating feedback from others and hours of work but such writing is a huge value add to the world.
I'm not too knowledgeable on how these deals work, but figured someone on HN would know:
A quick Google search shows that Twilio's market cap is currently $7.4 billion. Does this $2 billion "all-stock" transaction mean that they are giving away over a quarter of the company to pay for this acquisition? Or how else should I read this?
> Auto-encoders are overplayed, mostly because they're a pretty easy intro ML project.
I think you mean "normal" autoencoders, like denoising autoencoders or the identity autoencoder that are used for feature learning. Note that variational autoencoders are not really autoencoders in that sense. They are called “autoencoders” only because the final training objective that derives from the probabilistic setup does have an encoder and a decoder, and resembles a traditional autoencoder.
Traditional autoencoders are the common intro projects used for representation learning and to bootstrap other networks, not variational autoencoders.
The insight that made it possible for me to grasp VAEs was digging into the probabilistic setup that leads to this formulation. The neural networks are "just" a powerful function approximators applied on top of this probabilistic framework.
For people that know more about web security than I: Is there a reason it isn't good practice to hash the password client side so that the backends only ever see the hashed password and there is no chance for such mistakes?
I've mentioned this on HN before but I think its still relevant to people interested in learning ML who feel they are behind on the math. If, like me, you can't sit thru lots of pages of mathematics text and instead prefer that a human explains it to you via videos that you can replay, here is a list of courses that take you from basic algebra and pre-calculus math all the way to the concepts you need to understand the principles behind the most advanced ML algorithms. All explained by very energetic people who are experts in their fields, and starting from the very basics.
This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience:
(The first two are "MOOCs" recorded in the 1970s! probably the first ever recorded MOOC, even before the internet, and the lecturer is absolute gold)
While I am not a fan of Facebook myself for the all reasons recently talked about and the addicting nature of it, yes the self updating address book part is nice. Here's what I did: I got a Chrome extention[1] that unfollows all my friends. I've also unliked every Facebook Page that I used to like. This means my feed is now totally empty. When I login from anywhere there is nothing in my feed. Also I rarely ever get notifications, except when someone adds me as a friend. This way the feed is gone, there's nothing to be addicted to (feed was my main addiction), its very hard to get back because it involves re-following 100s of people which my lazy mind won't do and Facebook doesn't make any money off me (no feed, no ads). So I still get to use it as a address book.
Plus Codes is a "system is based on dividing the geographical surface of the Earth into tiny ‘tiled areas’, attributing a unique code to each of them. "
I don't think they want it to be a different service from Search (like Google Flights is). It's more that when you search for something like "jobs near me" you get these job results, just like the custom restaurant search/flight search from google.com
If you find it easier to keep at it and learn from lecture videos instead of from textbooks, here's a math curriculum of lecture videos I've curated. This covers calculus, linear algebra, probability, statistics, convex optimization and a math for ML course thrown in for the HN audience: