
Ask HN: Best way to get started with AI? - hackathonguy
Hey guys -<p>I&#x27;m a intermediate-level programmer, and would like to dip my toes in AI, starting with the simple stuff (linear regression, etc) and progressing to neural networks and the like. What&#x27;s the best online way to get started?<p>Thanks!
======
hal9000xp
I'm in the same boat. For long time, I was interested in AI but at the same
time intimidated by math. I'm relatively comfortable with discrete mathematics
and classical algorithms and at the same time calculus and linear algebra is
completely foreign to me. Also, I do not accept way to learn ML without good
understanding of core principles behind it. So math is a must.

A few months ago, I stumbled upon very amazing YouTube Channel _3Blue1Brown_
which explains math in very accessible way and at the same time I got feeling
that I finally started understanding core ideas behind linear algebra and
calculus.

Just recently he published 4 videos about deep neural networks:

[https://www.youtube.com/watch?v=aircAruvnKk](https://www.youtube.com/watch?v=aircAruvnKk)

[https://www.youtube.com/watch?v=IHZwWFHWa-w](https://www.youtube.com/watch?v=IHZwWFHWa-w)

[https://www.youtube.com/watch?v=Ilg3gGewQ5U](https://www.youtube.com/watch?v=Ilg3gGewQ5U)

[https://www.youtube.com/watch?v=tIeHLnjs5U8](https://www.youtube.com/watch?v=tIeHLnjs5U8)

So my fear of ML was gone away and I'm very _excited_ to explore whole new
world for neural networks and other things like support vector machines etc

~~~
skytreader
Worth noting that 3Blue1Brown also did a series on linear algebra which is
eye-opening to say the least. Playlist at:

[https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)

Even if you think you grok matrices, have a go at the first few videos of that
playlist, if just for the visualization. It really helped me see what matrices
(and operations on matrices) represent!

~~~
brandonhsiao
3Blue1Brown is a treasure. The production value is excellent, and he's great
at taking seemingly uninteresting ideas and painting a beautiful picture to
connect them in twenty minutes. I used to go through a video before falling
asleep each night.

------
binarymax
I highly recommend Andrew Ng's Coursera courses for both Machine Learning and
Deep Learning. Good for beginners, Math is taught along with the course, and
gets you a solid foundation:

[https://www.coursera.org/learn/machine-
learning/](https://www.coursera.org/learn/machine-learning/)

[https://www.coursera.org/learn/neural-networks-deep-
learning...](https://www.coursera.org/learn/neural-networks-deep-learning/)

~~~
hackathonguy
Thank you! Should I start with the Machine Learning one?

~~~
binarymax
At your level yes, I would recommend starting with the ML course. It is really
beneficial to understanding how the mathematics work.

The two most important things to remember, since the courses are challenging:
1) don't be in a hurry, and 2) don't give up! Take the time to learn every
detail presented, do the optional exercises, and dig deep.

~~~
xeromal
It's definitely challenging. The math and just seeing the complicated formulas
really push me, but the reward is good too. I'm tired of pushing pixels and
doing some meaty stuffy like ML is a nice change of pace.

------
alexmuro
Personally I recommend Stanford CSI 231n
[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

Its specifically geared towards visual recognition, but it starts with the
basics of machine learning and moves on to feed forward nets and covnets and
covers RNNs and attention towards the end.

The assignments are a great set of jupyter notebooks that really get your
hands on the material and you can find a number of peoples complete
assignments on github just by searching.

The lectures are available online as well
[https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-z...](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-
zLfQRF3EO8sYv)

I've done hinton's and Ngs courses and as someone who already has a non-ai
development background I found this to be the best introduction. Its really an
extension of Andrej Karpathy's Neural Nets for Hackers
([http://karpathy.github.io/neuralnets/](http://karpathy.github.io/neuralnets/))

~~~
Danilka
Did you signup for the course, or just followed what's available on the page?
It doesn't seem like there is any starting date there.

------
brandonhsiao
It's really important not to skip the math. As a friend once said to me, doing
deep learning without understanding the math is like gambling. It's fine to
initially take a more practical, project-based approach for the sake of
staying motivated, and you'll retain things better if you have project goals
in mind, but, the math is that important.

The good news is that compared to other technical fields, the math is also
relatively shallow. Here are some good resources that you don't need more than
calculus/linalg for (I've used all of them and they got me off the ground):

[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

[http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/)

[http://course.fast.ai/](http://course.fast.ai/)

Once you feel confident, the _Deep Learning_ book is more math-heavy, but it
is really very good. The authors are more or less deep learning gods. It'll
teach you a tremendous amount about how/why neural nets work and the
principles used to discover new architectures, and gain a strong intuition for
how to use neural nets as a tool. Read it slowly---unless you're already good
at math, it takes a while to get through. Don't skip the first five chapters.
Use Google and Wikipedia to pick up concepts you don't understand along the
way instead of skipping over them (it will bite you later).

------
kmax12
It somewhat depends on if you are looking to build AI to address business
problems or if you are more interested in the type of AI work you see
companies like Google discussing.

I can speak to what "AI" means for most businesses outside Top Tech which more
frequently work with tabular, relational, or log data rather than image and
text. For these companies, this is what you need to learn how to do

    
    
       1. Define a prediction problem and extract labels
    
       2. Organize and clean the data for prediction
    
       3. Perform feature engineering by applying domain expertise
    
       4. Apply an off-the-shelf open source machine learning algorithm like a random forest
    

Assuming you have access to data and programming skills to clean your data,
defining prediction problems and performing feature engineering are the most
important skills you have to pick up. For machine learning you can you use
open source libraries like scikit-learn or tensorflow.

At my company, we've noticed a lot of programmers are intimated by the feature
engineering step in particular, so we tried to make it easier by creating an
open source library called Featuretools [0].

[0]
[https://github.com/featuretools/featuretools](https://github.com/featuretools/featuretools)

~~~
mtrn
Thanks for the featuretools project, it sounds really useful. Is it Python 2
only?

~~~
kmax12
the latest release is for python 2.7 only. we have a pull request [0] up right
now for python 3 that passes all the automated testing.

[0]
[https://github.com/Featuretools/featuretools/pull/16](https://github.com/Featuretools/featuretools/pull/16)

------
smortaz
Great resources in the replies. If you want an environment to run code in w/o
much setup, try our free service:

[https://notebooks.azure.com](https://notebooks.azure.com)

it has Py2, Py3, R, F#, anaconda, TF, CNTK, etc. pre-installed.

There are some ML tutorials on it already + you can use the "load from github"
feature to load, run, edit, ... many of the great tutorials already on github.

Other similar environments include colab by google and cocalc.

#Disclaimer: Microsoft

~~~
yexponential
Interesting, didn't know about this, thanks for sharing

------
skadamat
I'm involved with a startup that's specifically tackling this very problem --
how do you learn the theory & application of machine learning quickly
(especially if you already know programming well). We teach using diagrams and
interactive coding exercises in the browser: www.dataquest.io

If you already know Python, you could dive straight into machine learning
([https://www.dataquest.io/course/machine-learning-
fundamental...](https://www.dataquest.io/course/machine-learning-
fundamentals)) and work your way upto calc / lin al, linear regression,
decision trees, neural nets, etc.

If you want to get a taste without signing up, you can check out our blog
posts that preview the course (like this one:
[https://www.dataquest.io/blog/machine-learning-
tutorial/](https://www.dataquest.io/blog/machine-learning-tutorial/))

Happy to answer any questions over DM or email (srini@ourdomain).

------
anothertraveler
1\. Start with the fast.ai courses. It's an applied deep learning course using
state of the art techniques. 2\. For classical machine learning (regression,
etc...), Andrew Ng's course on Coursera is widely considered "the basics" 3\.
As you progress, check out CS231 and CS224 from Stanford for state of the art
image processing and natural language processing techniques. The lecture
videos are on YouTube and the course assignments are available online. The
third course I recommend is Geoffrey Hinton's neural networks course on
Coursera (he is one of the most important researchers in the field). 4\. If
you're an application engineer, focus on using existing tooling to build cool
projects. Keras and scikit-learn are great out of the box tools. 5\. If you
are more research oriented, you can start reading papers. In Silicon Valley,
there's a meet up group that reads papers every Monday and tries to implement
the algorithms in the paper. It takes a while to get to this level, but try
not to get overwhelmed. Experts spend 7 years studying this stuff full time to
get a PhD. 6\. You really don't need much math to get started with ML. A high
school understanding of calculus and some basic understanding of numerical
optimization are the two main concepts you need to know. If you want to get
into the research, there'll come a time when you will need more advanced math,
but in my experience you can pick that up as you go along if you are curious.

Maybe you could start an AI study group online? The Silicon Valley study group
was great, but I was just visiting.

------
wonder_bread
If TensorFlow is what you're interested in I personally found "Hands-on
Machine Learning with SciKit-Learn and TensorFlow by Aurélien Géron" to be the
best introduction after introducing myself to the subject with Siraj's YouTube
videos

[https://www.amazon.com/Hands-Machine-Learning-Scikit-
Learn-T...](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-
TensorFlow/dp/1491962291/ref=sr_1_2?ie=UTF8&qid=1510606358&sr=8-2&keywords=machine+learning+green)

------
projectramo
AI != ML

For AI, I would take the Udacity AI courses.

For ML, I would take the Udacity ML courses.

I take a lot of different online courses, I have no affiliation with Udacity,
but their courses are just too good.

I studied AI (focused on ML) in a decent grad school (and I like to think I
had the best teachers there), and I think the quality of the courses is
comparable.

~~~
aalleavitch
Isn't AI just applied ML?

Or is it an operant/classical conditioning sort of thing, where AI is
specifically about training programs to act rather than to perceive/categorize
things?

I suppose you can have AI that incorporates no ML (like most video game AI),
but I'd imagine that will become vanishingly rare in the future.

~~~
randcraw
In brief, AI _uses_ existing knowledge and/or heuristics (to solve problems
that lack a closed-form solution), while ML _acquires_ knowledge and
heuristics toward the same end, with the added goal of improving performance
as it learns and adapting to changing conditions.

Traditionally, AI has been divided into distinct subfields (e.g. search,
planning, natural language and speech processing, game playing, computer
vision, robotics, knowledge representation, expert systems, logic, and ML).
Today, ML is employed in all AI subfields, but until recently, most subject
matter in each AI subfield had been unrelated to ML. In the past decade
especially, that's changed as deep learning and probabilistic methods have
gained mindshare and now are largely unavoidable when tackling AI-related
problems.

In general, AI's subfields have focused on identifying fundamental obstacles
and important features in their own problem domain and developing appropriate
techniques that operate on those features when solving problems (like using
object recognition and localization to solve vision problems like autonomous
driving). I suspect AI's past emphasis on feature engineering has faded as NN-
based ML has risen.

------
lottin
In my opinion the best way to get started is first study statistical inference
and modelling, in particular linear regression and the method of maximum
likelihood. This will give you a critical eye later on for discerning when
it's a good idea to actually use ML and when it's not (an important skill that
apparently is in very short supply these days ;).

------
mswen
Introduction to Statistical Learning with Applications in R [http://www-
bcf.usc.edu/~gareth/ISL/](http://www-bcf.usc.edu/~gareth/ISL/)

Statistical Rethinking by Richard McElreath gives a good introduction to
Bayesian approaches to statistical analysis
[https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA](https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA)

------
seriousssam
My friend and I wrote this guide called ML4Humans.
[https://medium.com/machine-learning-for-humans/why-
machine-l...](https://medium.com/machine-learning-for-humans/why-machine-
learning-matters-6164faf1df12)

People like you are our primary audience :) it should take you exactly where
you want to start and take you a good chunk of the way to where you wanna get.

Please check it out

------
jedanbik
Siraj Raval does a great job of explaining AI topics with fun, fresh, and easy
to understand topics and examples:

[https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A)

Here's a recent video where he talks about how to create new Pokemon with
Generative Adversarial Networks
([https://en.wikipedia.org/wiki/Generative_adversarial_network](https://en.wikipedia.org/wiki/Generative_adversarial_network)).
Nice contrast from the usual MNIST dataset, especially if you want to be
inspired to think about novel ways to apply this stuff:

[https://www.youtube.com/watch?v=yz6dNf7X7SA](https://www.youtube.com/watch?v=yz6dNf7X7SA)

------
indescions_2017
Intro to AI

[https://www.udacity.com/course/intro-to-artificial-
intellige...](https://www.udacity.com/course/intro-to-artificial-intelligence
--cs271)

Machine Learning

[https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning)

The Pacman programming exercises in python

[http://ai.berkeley.edu/project_overview.html](http://ai.berkeley.edu/project_overview.html)

And the Kaggle Titanic Survivability dataset

[https://www.kaggle.com/c/titanic](https://www.kaggle.com/c/titanic)

But if you desire an even gentler intro. Try Daniel Shiffman's Nature of Code
in P5

[http://natureofcode.com/](http://natureofcode.com/)

best of luck ;)

------
mceoin
fast.ai - it's free, and there's a low level of assumed knowledge from the
outset.

~~~
icc97
their 'deep learning in excel' [0] is a great simplification of the
fundamentals.

[0]:
[https://www.youtube.com/watch?v=qnoLMkosHuE](https://www.youtube.com/watch?v=qnoLMkosHuE)

[1]: [http://www.fast.ai/2016/10/08/teaching-
philosophy/](http://www.fast.ai/2016/10/08/teaching-philosophy/)

------
rwieruch
I am sitting in the same boat. Being a web developer for a couple of years, I
wanted to try out a different domain. So I started to take Andrew Ng's course
on Coursera [0]. Highly recommended. I supplement the course with audio and
text by listening to the Machine Learning Guide Podcast [1] and by reading The
Master Algorithm [2].

In addition, I started to apply my learnings in JavaScript [3]. Even though
it's not the best language for ML, it makes it simpler to learn only one new
thing and stick to known technologies for the rest. I have lined up ~7
articles about ML in JavaScript, so if you are interested, you can keep an eye
on it :)

\- [0] [https://www.coursera.org/learn/machine-
learning/](https://www.coursera.org/learn/machine-learning/)

\- [1] [http://ocdevel.com/podcasts/machine-
learning](http://ocdevel.com/podcasts/machine-learning)

\- [2] [https://www.goodreads.com/book/show/24612233-the-master-
algo...](https://www.goodreads.com/book/show/24612233-the-master-algorithm)

\- [3] [https://www.robinwieruch.de/linear-regression-gradient-
desce...](https://www.robinwieruch.de/linear-regression-gradient-descent-
javascript/)

------
lee101
I got started being into algorithms and then making games with ai opponents
like [http://bitmultiplayerchess.com](http://bitmultiplayerchess.com),
[http://wordsmashing.com](http://wordsmashing.com) I took andrew ng’s coursera
machine learning course which i would highly recommend, also his deep learning
course is worth it too :)

His course inspired me to create a cryptocurrency trading bot which i spun
into a business offering forecasting for altcoin markets:
[http://BitBank.nz](http://BitBank.nz) \- Crypto Market Predictions with
Machine Learning

I managed to make much more successful forecasts by understanding the
fundamentals taught in that course like under-fitting and over-fitting and how
to visualize whats happening by plotting a learning curve ect.

The forecasting algorithm really just applies the fundamentals thoroughly in
perhaps a novel way, e.g. some features we compute at the current time include
the linear regression of trades over time weighted by their amount

So its definitely worth the investment i think :) try and apply the teaching
to solve a real world problem which i think is the interesting part, although
you’ll end up doing a lot of data engineering you’ll savor the AI/ML part even
more and start to appreciate strategies for how you can improve your
performance in your case and test them out.

Having a play around with the create your own deep neural net at
playground.tensorflow.org is pretty helpful, try and conceptualize what youve
been taught in the courses by playing around with that, e.g. add more
layers/breadth to your network to watch it get more and more powerful and
begin to overfit when you add noise to your data ect.

------
dmode
Just hijacking this question for my benefit as well. I am a product manager in
enterprise focused software. I want to transition to the world of AI. Is
Udacity's $600 Deep Learning Nano degree worth it ?

~~~
palatalizacija1
I don't know about Deep Learning Nanodegree, but I took Machine Learning and
AI Nanodegrees at Udacity and they are definitely worth it. I would not
recommend them to total beginners in the field. You need to have at least some
experience with data science and Python to be able to follow along. Do some
free courses on Udacity, Coursera, EdX and other platforms, try to implement
these algorithms with your own data and problems and then take the Nanodegree
to fill the gaps.

~~~
newbear
Did you find a job after?

------
gncb
I was at the same point as you until I discovered the new Andrew Ng course on
deep learning [1]

It's a good structured way to learn the core of ML while learning about Neural
Networks and without having to become and linear algebra expert which for most
people including like me was a deal breaker with other courses. The timing is
great too as ML now is so much different than it was 2-3 years ago.

[1] [https://www.coursera.org/specializations/deep-
learning](https://www.coursera.org/specializations/deep-learning)

------
mooneater
I think being effective in ML requires both theory, and practical knowledge
you only get by doing.

Andrew Ng's ML course quickly provides a base in theory.

Ideally you couple that with some empirical work.

For that, I think sklearn is the best starting point (assuming you go down the
python path). Modify some sample code and make a few simple models. Sklearn
provides an excellent framework across all kinds of models (including deep
learning if you use say keras.wrappers.scikit_learn), and can play well with
pandas.

There are lots of practical concerns that come up that are not covered in
intro ML courses.

------
hackernewsacct
As a follow up: I want to pursue a math degree study. What course titles and
textbooks starting at the calculus level do you guys recommend? I want enough
math chomps to then go onto a PhD in ML.

~~~
dominotw
Spivak calculus. There is nothing like it. You will learn how to think in
math, not just calculus.

~~~
anothertraveler
Whoa! I was just talking about this text book the other day! I think it's out
of print these days, but it's a hidden gem!

------
austenallred
Possibly not what you're looking for (certainly not the cheapest option), but
we (Lambda School - YC S17) just announced a live, remote class that trains
engineers in AI & ML during weekday evenings for six months.

The next one starts in January, and is taught by an MIT grad that taught a
similar course at MIT.

[https://lambdaschool.com/artificial-
intelligence](https://lambdaschool.com/artificial-intelligence)

~~~
amigoingtodie
Would you do 20% for a salary of $100k or above?

What percentage would you do for $100k?

~~~
austenallred
It's still/always 17%, but it would cap out at $20,000 if above ~117k, so it
would effectively be less than 17% on an annualized basis.

Or paying up-front/in monthly payments is $1041/month for 12 months.

------
aficionado
[https://bigml.com/education/videos](https://bigml.com/education/videos)
[https://bigml.com/ml101/](https://bigml.com/ml101/)
[https://bigml.com/tutorials/](https://bigml.com/tutorials/)

------
aalleavitch
I've been going through this course:
[https://www.commonlounge.com/community/9dcdd386cc28446695305...](https://www.commonlounge.com/community/9dcdd386cc28446695305db00d2de532)

It's a bit more cursory and mostly just a collection of articles/papers, but
it has the benefit of not being paced like a university course.

------
leowoo91
I like following article as I find it one of the easiest introduction to
neural networks:

[https://medium.com/technology-invention-and-more/how-to-
buil...](https://medium.com/technology-invention-and-more/how-to-build-a-
simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1)

------
partycoder
The AI for humans series is some reasonable, high level approach.
[http://www.heatonresearch.com/aifh/](http://www.heatonresearch.com/aifh/)

After you've got a grasp of what these things are doing then you can move into
the how. For that you will need some math background, with emphasis in
calculus and probability.

After that, you can take a look at PRML. [https://www.amazon.com/Pattern-
Recognition-Learning-Informat...](https://www.amazon.com/Pattern-Recognition-
Learning-Information-Statistics/dp/0387310738)

Some people might prefer seeing things from another approach.
[http://pgm.stanford.edu/](http://pgm.stanford.edu/)

Good luck.

------
deepnotderp
For deep learning, my two favorite nominees are:

1\. Hugo Larochelle's Deep Learning course available on YouTube

2\. Depending on how much math you like, Nando de Freitas's Deep Learning
course (also on YouTube) is also superb.

------
balp
I liked the tutorials at Python Programming, sometimes the python details goes
a bit fast and there are typos but over all it's the one that got me most
understanding the practical parts.

[https://pythonprogramming.net/machine-learning-tutorial-
pyth...](https://pythonprogramming.net/machine-learning-tutorial-python-
introduction/)

------
chestervonwinch
I know you say you'd like to learn online, but I highly recommend picking up
Duda and Hart's _Pattern Classification_ to have a theoretical complement to
the "hands on", programming type introductions. It's a very accessible intro
to the topic, but also covers a lot of material in depth -- in particular, the
topics you mention.

------
andyjohnson0
I'm currently working through "Deep Learning: A Practitioner's Approach" by
Adam Gibson and Josh Patterson. Its a couple of years old but seems like a
good book, and I'm certainly learning a lot. It doesn't consider some of the
newer tooling, like TensorFlow, but the fundamentals plus a decent amount of
theory are all covered.

~~~
randcraw
What makes you say the book is 2 years old? Its publication date is Aug 1,
2017. It's based on Deeplearning4J and ND4J rather than TF, but that doesn't
date it necessarily.

~~~
andyjohnson0
You're right, it is August 2017. I bought it from Amazon UK and they list the
date as November 2015 [1] for some reason. I should have checked the book
before posting. I've flagged the error with Amazon UK.

[1] [https://www.amazon.co.uk/Deep-Learning-Practitioners-Adam-
Gi...](https://www.amazon.co.uk/Deep-Learning-Practitioners-Adam-
Gibson/dp/1491914254)

------
yters
I took a grad ML course based on this book:
[https://www.amazon.com/dp/B0759M2D9H](https://www.amazon.com/dp/B0759M2D9H)

It teaches you the foundational theory behind ML, and shows how the fancier
stuff is built on it. Good to know the foundations, so you can branch outside
of predefined ML techniques.

------
scoot
I can't speak to the rest of the content, but I found the introduction in the
course accompanying the recently announced gluon library to be both
comprehensive and comprehensible at the same time.

[http://gluon.mxnet.io/](http://gluon.mxnet.io/)

------
aqsheehy
Whenever you see a new term you don't know about watch/read 3 videos/article
on it

------
ahamedirshad123
I find this helpful. All links in one place
[https://www.springboard.com/learning-paths/data-
analysis/](https://www.springboard.com/learning-paths/data-analysis/)

------
source99
My recommendation is the fast.ai course by Jeremy Howard. His explanations are
amazing and the practical usefulness is immediate.

[http://course.fast.ai/](http://course.fast.ai/)

------
godelmachine
In my humble opinion, there's no better way to start than with the classic
book - " Artificial Intelligence : A Modern Approach " by Russel Norvig.

------
stonepresto
Berkeley has an free videos/slides, combined with exams, projects, and
homework.

Link:http: //ai.berkeley.edu/home.html

------
erik14th
this one starts with simple stuff MIT 6.034 Artificial Intelligence:
[https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP...](https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP63gFHB6xb-
kVBiQHYe_4hSi)

------
minimaxir
If you’re genuinely a novice programmer/lesser background in linear algebra,
AI should be the _last_ thing on your mind. Any attempts at a shortcut will
enhance the difficulty in learning AI, and being able to code things besides
simple examples. (which is why I am annoyed by many of the ML MOOCs which are
targeted toward novice programmers)

~~~
dagw
I disagree. There is nothing magic or hard about basic ML. You can do real
work with only some basic linear algebra and programming skills. Sure you
won't be doing novel deep learning on 100 terabyte datasets, but most problems
aren't that anyway.

~~~
aalleavitch
Yeah, and to be honest while it's always important to be able to understand
the math behind what you're doing, you can easily get started with just
understanding what these algorithms do and why, and then work to expand your
knowledge of the math over time from there.

------
allenleein
Here are the resources I found useful:
========================================== Advices from Open AI, Facebook AI
leaders

Courses You MUST Take:

Machine Learning by Andrew Ng ([https://www.coursera.org/learn/machine-
learning](https://www.coursera.org/learn/machine-learning)) /// Class notes:
([http://holehouse.org/mlclass/index.html](http://holehouse.org/mlclass/index.html))

Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory
than the Coursera class but it is still relevant for beginners.

([https://work.caltech.edu/telecourse.html](https://work.caltech.edu/telecourse.html))

Neural Networks and Deep Learning (Recommended by Google Brain Team)
([http://neuralnetworksanddeeplearning.com/](http://neuralnetworksanddeeplearning.com/))

Probabilistic Graphical Models ([https://www.coursera.org/learn/probabilistic-
graphical-model...](https://www.coursera.org/learn/probabilistic-graphical-
model...))

Computational Neuroscience ([https://www.coursera.org/learn/computational-
neuroscience](https://www.coursera.org/learn/computational-neuroscience))

Statistical Machine Learning
([http://www.stat.cmu.edu/~larry/=sml/](http://www.stat.cmu.edu/~larry/=sml/))

From Open AI CEO Greg Brockman on Quora

Deep Learning Book
([http://www.deeplearningbook.org/](http://www.deeplearningbook.org/)) ( Also
Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep
learning engineering (except reinforcement learning). by Greg

2\. If you’d like to take courses: Linear Algebra — Stephen Boyd’s EE263
(Stanford) ([http://ee263.stanford.edu/](http://ee263.stanford.edu/)) or
Linear Algebra (MIT)

([http://ocw.mit.edu/courses/mathematics/18-06sc-linear-
algebr...](http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr...))

Neural Networks for Machine Learning — Geoff Hinton (Coursera)
[https://www.coursera.org/learn/neural-
networks](https://www.coursera.org/learn/neural-networks)

Neural Nets — Andrej Karpathy’s CS231N (Stanford)

[http://cs231n.stanford.edu/](http://cs231n.stanford.edu/)

Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287
(Berkeley)

[https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/](https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/)

Deep RL — John Schulman’s CS294–112 (Berkeley)
[http://rll.berkeley.edu/deeprlcourse/](http://rll.berkeley.edu/deeprlcourse/)

~~~
anothertraveler
This list is solid, and could keep you busy for a few years.

------
bra-ket
Learn about human intelligence

