Category Archives: Uncategorized

List of Bitcoin/Blockchain Resources

As AIDL grew, once in a while people would talk about blockchain would affect AI or deep learning.  Currently it is still a long shot, but blockchain by itself is a very interesting technology and it deserves our notice.

Here are some resources you may use to learn about blockchain.   Unlike "Top 5 List" for AIDL,  I don't really understand the technology too well.  But also unlike "List of Neuroscience MOOC", Greg Dubela did give me a lot of recommendations on what you should learned up.  Thus this post is also used as a resource post in "Blockchain Nation".

Introductory Videos:

  • (2 minute) This video: explaining the purpose of blockchain in 2 minutes, and the promise it makes.
  • This 6-part series from Dash School is a great introductory series on what Blockchain is, how it is governed, and several fundamental concepts.   Greg highly recommend the series.


Blockchain is still a new development, so it's harder to find MOOC which can teach you the whole thing in entirety.  We found there are couple of exceptions:


Visualizing Blockchain

Different Cryptos: (under construction)

Learning blockchain these days usually means you know different the characteristics of different coins.  Here are list of interesting ones.

  • Bitcoin
  • Litecoin
  • Ripple
  • Ethereum Classic
  • Ethereum
  • Dogecoin
  • Freicoin

As I said before, we are really no expert on the topic.  But as of 20170705, I am taking the Princeton class and I found it quite promising and get into the detail of how blockchain really works.

To be reviewed:

  • Someone also brought up University of Nicosia's Introductory MOOC on bitcoin.  I haven't see too much review yet.  So let's decide later then.
  • Khan Academy:
  • Berkeley "Dive Deep into Ethereum"
  • Udemy's Bitcoin class:
  • A list of very useful Bitcoin classes:
  • A series from CRI :

Notes on fasttext

Some gist about fasttext:

  • Basically 3 packages, wordvector, text classification and compression.
  • Text classifications is really comparable with other deep methods.  Another Web's wisdom is here.
  • Running the tasks are trivial for proficient unix users.  So I don't want to repeat them here.  The examples also run end-to-end and they are fast.
  • Unlike what I thought though, fasttext doesn't quite setup a deep-learning-based word-classification, but as I said, that's not the point.
  • Compression was known to be so good such that it can fit to be embedded devices.
  • Users also got granted patents to use the source code freely.  So good stuffs.

Some other nice resources one can follow:


Some Quick Impression on MIT DL4SDC Class by Lex Friedman

Many of you might know about the MIT DL4SDC class by Lex Friedman. Recently I listen through the 5 videos and decide to write a "quick impression" post. I usually write these "impression posts" when I only gone through some parts of the class' material. So here you go:

* 6.S094, compared to Stanford cs231n or cs224d, is a more a short class which takes <6 hours to watch through all materials.

* ~40-50% of the class was spent basic material such backprop or Q-learning. Mostly because the class is short, the treatment of these topics feels incomplete. e.g. You might want to listen to Silver's class to understand systematically about RL and the place of Q-learning. And you might want to listen to Kaparty's at cs231n to know the basic of backprop. Then finish Hinton's or Socher's to completely grok it. But again, this is a short class, you really can't expect too much.

Actually, I like Friedman's stand on these standard algorithms: he asked audience tough questions on whether human brain ever behave as backprop or RL.

* The rest of the class is mostly on SDC, planning with RL, steering with all-in-one CNN. The part which is gem (Lecture 5) is Friedman's own research on driver's state. If you don't have too much time, I think that's the lecture you want to sit through.

* Now, my experience doesn't quite include the two very interesting homeworks, DeepTraffic or DeepTesla. Both I heard great stories from students. Unfortunately I never try to play with them.

That's what I have. Hope the review is useful for you. 🙂

My Social Network Policy

For years, my social networks don't follow a single theme.  For the most part I have varieties of interest and don't feel like pushing any news ....... until there is deep learning.   As Waikit and I started AIDL at FB with a newsletter and a youtube channel, I also start to see more traffic comes to thegrandjanitor as well. Of course, there are also more legit followers on Twitter.

In any case, here are couple of ways you can find me:
Facebook: I am private on Facebook. I don't PM, but you can always find me at the AIDL group.
LinkedIn:  On the other hand, I am very public on LinkedIn. So feel free to contact me at .
Twitter: I am quite public on Twitter
Plus: Not too active, but yeah I am there

Talk to you~


Good Old AI vs DNN - A question from AIDL Member

Redacted from this discussion at AIDL.

From Ardian Umam (shortened, rephrased):
"Now I'm taking AI course in my University using Peter Norvig and Stuart J. Russell textbook. In the same time, I'm learning DNN (Deep Neural Network) for visual recognition by watching Standford's Lecure on CNN (Convolutional Neural Networks) knowing how powerful a DNN to learn something from dataset. Whereas, on AI class, I'm learning about KB (Knowledge Base) including such as Logical Agent, First Order Logic that in short is kind of inferring "certain x" from KB, for example using "proportional resolution".

My question : "Is technique like what I learn in AI class I describe above good in solving real AI problem?" I'm still not get strong intuition about what I study in AI class in real AI problem."

Our exchange:

My answer: "We usually call "Is technique .... real AI problem?" GOAI (Good Old Artificial Intelligence). So your question is weather GOAI is still relevant.

Yes, it is. Let's take search as an example. More complicated systems usually have certain components in search. e.g. Many speech recognition these days still use Viterbi algorithm which is large-scaled search. NNMT type of technique still requires some kind of stack decoding. (Edit, was beam search, but I am not quite sure.)

More importantly, you can see many things as a search. e.g. optimization of a function, you can solve it by Calculus, but in practice, you actually use search algorithm to find the best solution. Of course, in real-life, you rarely implement beam search to optimization. But idea of search would give you better feeling many ML algorithms like."

AU: "Ah, I see. Thank you Arthur Chan for your reply. Yes, for search, it is. Many real problems now are still utilizing search approach to solve. As for "Knowledge, reasoning" (Chapter 3 in the Norvig book) for example using "proportional resolution" to do inference from KB (Knowledge Base), is it still relevant?"

My Answer: "I think the answer is it is and it is not. Here is a tl;dr answer:

It is not: because many practical systems these days are probabilistic. So it makes Part V of Norvig's book *feel* more relevant now. Most people in this forum are ML/DL fans. That's probably the first feeling you should have in these days.

But then, it is also relevant. In what sense? There are perhaps 3 reasons. First is it allows you to talk with people who learn A.I. from the last generation, because people in their 50-60s (aka, your boss) learn solving AI problem with logic. So if you want to talk with them, learning logic/knowledge type of system would help. Also in AI, no one knows what topic would revive. e.g. Fractal is now the least talked-about topic in our community now. But you never know what happen in the future 10-20 years. So keep ing breath is a good thing.

Then there is the part of how you think about search, in both Norvig and Russell's books, the first few search problem is to solve logic problem such as first-order logic, chess. While they are only used in fewer system, compare to search which requires probabilities, they are much easier to understand. e.g. You may heard of people in their teens write their first chess engine, but I heard no one write (good) speech recognizer or machine translator before grad school.

The final reason is perhaps more theoretical: many DL/ML system you use, yeah... .they are powerful, but not all of them are making decision human understand. So they are not *interpretable*. That's a big problem. So it is still a research problem of how to link these system to GOAI-type of work."

Should You Learn Lisp?

(Redacted from a post on AIDL.)

Learning programming languages, like human languages, or generally different skills, is a way to enlighten you. LISP is a cool language because it does things differently. So sure, in that sense, Lisp worths your time.

On the other hand, if you do want to learn modern-day A.I. though, perhaps probability and statistics are the first "language" you want to learn well. As one member, Ernest Szeto said, nowadays A.I. usually use at least some probability-based logic. And if you think probability and statistics as a language, they are fairly difficult to learn on their own.

And yes, at AIDL, we recommend python as the first language, because it allows you to use several stacks in deep learning. You can also use R and java, but notice that there will be a gap between your work and what many people are doing.


An AIDL member's question at 20170329.

(Redacted from a AIDL's discussion.)

Q (First asked by Shezad Rayhan,  Redacted): "I bought some books on ML,Deep Learning ,RL seeing the reviews on amazon and quora.
[Arthur: the OP then listed out ~10 books on different subjects such as DL, ML, RL.]" .....

"I saw few lectures of Geoffrey Hinton's Neural networks course but not sure which text book has similarity with his lectures."

A: "Good question. Thanks for writing it up. My 2cts:

You bought too many books. Here are the few books to focus on

  • Python Machine Learning (Sebastian Raschka )
  • The Elements of Statistical Learning (Trevor Hastie,Robert Tibshirani,Jerome Friedman )
  • Machine Learning A probabilistic perspective (K Murphy)
  • Pattern Recognition and Machine learning (Bishop)
  • Deep learning (Ian Goodfellow,Yoshua ,Aaron Courville)
  • Neural networks and learning machines(Simon Haykin)

I never read Raschka's and Murphy's book but there are many good comments. Raschka's books is more for practical use of machine learning so that's probably the best place to start. For other 5, if you can read through one of them with ease, you should already be able to get a job or do research somewhere.

To your question about Hinton's: not every lectures come with a textbook. Hinton's class is unique in the sense that *he* represents a point of view, so you have to delve into his or his students' paper.


Test Pet Groups I started for Fun

As you might know, I have been administering AIDL for a while, AIDL has a rather strict (self-inflicted? :)) posting guidelines so many posts such as general neural science, AGI and self-driving are usually deleted.

Since I am also quite interested in those topics, I decide to start two more test groups as my own link dumps.  They are much less elaborated than AIDL and frankly my knowledge in those topics are amateurish, but it is a fun place if you want to hang out and talk about fancier topics.   Here is their links:

Computational Neuroscience and Artificial General Intelligence:

Self Driving Car with Deep Learning: