I was watching a twitch livestream where he was coding an RL thing. His code was just wrong (I paused it and looked through it), but it compiled anyways and started outputting stats, so he declared "I'm such a baller! It's learning!" and then quickly concluded the program. It's one thing to find his style annoying, but he is neither a strong thinker nor coder.
I've personally found him to be more of a "showman" and a youtube "star" rather than someone technically adept with data sciences. He is good at what he does - which is building cool things using cool tools/api.
But I wouldn't recommend him as a good resource to learn core ML from or figure out how stuff work internally.
I'm with the others on this. Never mind the cringe - he's all show, so much so I think he's bluffing (doesn't know ML). He amps up on "character" so much you're excited for the knowledge drop - when it comes, it's so fast and technical there's nothing to gain from it. The adage "if you can't explain something simply you don't understand it" applies. I was hoping he understood ML enough to boil things down; instead he spews equations and jargon so fast (1) you don't catch it, (2) I think he's just reading from a source. He doesn't go for essence, he goes for speed - and that's not helpful.
Again, the cringe isn't the problem directly; but that it's a cover for his bluff. The result is a not-newbie-friendly resource.
I just checked out the "About" section of his Youtube channel.
> I've been called Bill Nye of Computer Science Kanye of Code Beyonce of Neural Networks Osain Bolt of Learning Chuck Norris of Python Jesus Christ of Machine Learning but it's the other way. They are the Siraj Raval of X
He just pipes input through bunch of libraries that are available off the shelf. Does that produce a useful output? Sure. Could he write any of them himself, or explain how any of them work beyond a superficial overview? I doubt it.
"Learn AI the Hard Way". It's actually just reading a bunch of papers and trying to implement them, and anytime you don't understand something spend as much time as needed until you get it.
* Book: Hands-On Machine Learning w/ Scikit-Learn & TensorFlow (http://amzn.to/2vPG3Ur). Theory & code, starting from "shallow" learning (eg Linear Regression) on sckikit-learn, pandas, numpy; and moves to deep learning with TF.
Andrew Ng's tutorials[1] on Coursera are very good.
If you're into python programming then tutorials by sentdex[2] are also pretty good and cover things like scikit, tensorflow, etc (more practical less theory)
Just Q&A - no presentations. Study from whatever books (http://amlbook.com/ and http://www.deeplearningbook.org/ are popular in our group) or courses (Andrew Ng's are also popular) you like throughout the week and then show up with any questions you have. We've been meeting for a couple of months now and new folks are always welcome no matter where you are in your studies!
Online courses recommended in this thread are great resources to get your feet wet. If you want to actually be able to build ML powered applications, or contribute to an MLE team, we've written a blog post which is a distillation of conversations with over 50 top teams (big and small) in the Bay Area. Hope you find it helpful!
Firstly, while I think it's beneficial to learn multiple languages (python, R, matlab, julia), I'd suggest picking one to avoid overwhelming yourself and freaking out. I'd suggest python because there are great tools and lots of learning resources out there, plus most of the cutting edge neural networks action is in python.
Then for overall curriculum, I'd suggest:
1. start with basic machine learning (not neural networks) and in particular, read through the scikit-learn docs and watch a few tutorials on youtube. spend some time getting familiar with jupyter notebooks and pandas and tackle some real-world problems (kaggle is great or google around for datasets that excite you). Make sure you can solve regression, classification and clustering problems and understand how to measure the accuracy of your solution (understand things like precision, recall, mse, overfitting, train/test/validation splits)
2. Once you're comfortable with traditional machine learning, get stuck into neural networks by doing the fast.ai course. It's seriously good and will give you confidence in building near cutting-edge solutions to problems
3. Pick a specific problem area and watch a stanford course on it (e.g. cs231n for computer vision or cs224n for NLP)
4. Start reading papers. I recommend Mendeley to keep notes and organize them. The stanford courses will mention papers. Read those papers and the papers they cite.
5. Start trying out your own ideas and implementations.
While you do the above, supplement with:
* Talking Machines and O'Reilly Data Show podcasts
* Follow people like Richard Socher, Andrej Karpathy and other top researchers on Twitter
For those who like videos, I would highly recommend utilizing Andrew Ng's Coursera ML videos for step one. I found his lectures to be good high level overviews of those topics.
The course in general lacks rigor, but I thought it was a very good first step.
Andrew Ng's Coursera course is probably good for some backgrounds. But if your background is as someone who has mostly been programming for the last few years, I feel that Andrew Ng's course has two big drawbacks:
1. It's not very hands-on or practical. You won't actually get the feeling of building anything for a while.
2. It's very math oriented. If the last time you took a math class for your CS degree was a few years ago, you run the risk of not really remembering the background material well.
I'd personally recommend doing two things in parallel, if your background is in programming with less math training:
1. Look for a very hands-on/practical course to try out some examples.
2. At the same time, start refreshing (or learning) some maths that you might not remember, specifically, probability and statistics. Then after, Linear Algebra and maybe calculus.
I'm going to disagree with this about the difficulty of the math in Andrew Ng's course. Do you remember how to differentiate a function? Look up partial derivatives if you don't remember how they work, it shouldn't take longer than an hour. You're probably going to be fine.
If you never took calculus it's probably going to be hard going, but almost all modern machine learning requires basic calculus.
I would really recommend going through the first part of the course about linear regression if you haven't encountered it before, it was really eye opening for me.
Linear regression is incredibly important, but I think it's much better understood either practically (by implementing it or using it), or if you want to understand it mathematically, at the "end" of a statistics course. There's a reason that when learning probability/statistics, you usually encounter Linear Regression near the end of an introductory course, not in the beginning.
Again, this really depends on how mathematically competent you already are. I'm just basing this on how I felt coming to the course after having finished my degree about 10 years ago, therefore not really having most prob/statistics fresh in my mind.
You can certainly complicate the hell out of linear regression, but Andrew Ng introduces it in the setting of optimization/stochastic gradient descent, which I think is both mind blowing and a much simpler introduction than most statistics courses.
It's the very first bit of the course, I think everyone who is interested should try learning it. If not it's fine, but I wouldn't want anyone to not even try to spend a few hours on it because someone on the internet said it would be too hard.
That's certainly reasonable. And I totally agree with "try it out and gauge for yourself whether it's valuable for you".
My worry is that people will be put off from the field of machine learning if, 3 lessons into Andrew Ng's course, they will see that they don't understand anything, and that it's not practical to boot.
So my advice (generally applicable) is to try a few different things, because different resources click for different people.
This doesn't actually answer the question, but I always think that people who want to study neural nets should read Marvin Minsky's Perceptrons. It's an academic work. It's short. It's incredibly well written and easy to understand. It shaped the history of neural net research for decades (err... stopped it, unfortunately :-) ). You should be able to find it at any university library.
Although this recommendation doesn't really fit the requirements of the poster, I think it is easy to reach first for modern, repackaged explanations and ignore the scientific literature. I think there is a great danger in that. Sometimes I think people are a bit scared to look at primary sources, so this is a great place to start if you are curious.
Geoff Hinton's Coursera course was what got me into it. It's not for the faint of heart. I might recommend Andrej Karpathy's cs231n as a more up to date source today.
If you were to spend a year or so going through many of the resources presented here, and probably knew your stuff pretty well (or at least as well as you could after a year), would anyone actually give you a job?
Nobody is "given" a job; you "earn" a job by convincing the hiring manager that you can do what they need done.
If you're any good, and have good results to show and talk about, yes, you could totally be employed.
If you show that you're extra willing to do all the heavy data preparation and labeling work yourself as well as the infrastructure that runs the models, you'll have an even easier time. Most people just want to play with models, and believe data preparation is "beneath" them, but that's actually where the meat is and where the success of the model is made or destroyed.
It depends what sort of a job you have in mind. If you wanted the sort of job where you spend all day every day doing ML/DL/AI stuff then no, that's a pure research job and probably needs a PhD. But the life of an ordinary working data scientist isn't like that: you would spend 75% of your time acquiring and cleaning/pre-processing data (including all the organizational tasks of finding it and persuading people to give you it), 20% of your time trying to shepherd what you had created/discovered into a real, working production system, and maybe 5% if you are lucky on this sort of thing. You absolutely can learn everything you need to get to this level through MOOCs. The rest is down to your interview skills.
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[ 7.0 ms ] story [ 122 ms ] threadIt is quirky, funny and above all very short and crisp and gives you a quick overview of things. Most of his videos are related to AI/ML.
But I wouldn't recommend him as a good resource to learn core ML from or figure out how stuff work internally.
Again, the cringe isn't the problem directly; but that it's a cover for his bluff. The result is a not-newbie-friendly resource.
> I've been called Bill Nye of Computer Science Kanye of Code Beyonce of Neural Networks Osain Bolt of Learning Chuck Norris of Python Jesus Christ of Machine Learning but it's the other way. They are the Siraj Raval of X
I mean, seriously?
He just pipes input through bunch of libraries that are available off the shelf. Does that produce a useful output? Sure. Could he write any of them himself, or explain how any of them work beyond a superficial overview? I doubt it.
https://github.com/ChristosChristofidis/awesome-deep-learnin...
https://github.com/josephmisiti/awesome-machine-learning
* Book: Hands-On Machine Learning w/ Scikit-Learn & TensorFlow (http://amzn.to/2vPG3Ur). Theory & code, starting from "shallow" learning (eg Linear Regression) on sckikit-learn, pandas, numpy; and moves to deep learning with TF.
* Podcast: Machine Learning Guide (http://ocdevel.com/podcasts/machine-learning). Commute/exercise backdrop to solidify theory. Provides curriculum & resources.
If you're into python programming then tutorials by sentdex[2] are also pretty good and cover things like scikit, tensorflow, etc (more practical less theory)
[1] https://www.coursera.org/learn/machine-learning [2] https://pythonprogramming.net/data-analysis-tutorials/
Introduction to Statistical Learning http://www-bcf.usc.edu/~gareth/ISL/
Elements of Statistical Learning https://web.stanford.edu/~hastie/ElemStatLearn/
* https://www.udacity.com/course/intro-to-artificial-intellige...
* https://www.udacity.com/course/machine-learning--ud262
Deep Learning:
* Jeremy Howard's incredibly practical DL course http://course.fast.ai/
* Andrew Ng's new deep learning specialization (5 courses in total) on Coursera https://www.deeplearning.ai/
* Free online "book" http://neuralnetworksanddeeplearning.com/
* The first official deep learning book by Goodfellow, Bengio, Courville is also available online for free http://www.deeplearningbook.org/
Just Q&A - no presentations. Study from whatever books (http://amlbook.com/ and http://www.deeplearningbook.org/ are popular in our group) or courses (Andrew Ng's are also popular) you like throughout the week and then show up with any questions you have. We've been meeting for a couple of months now and new folks are always welcome no matter where you are in your studies!
https://blog.insightdatascience.com/preparing-for-the-transi...
Disclaimer: I work for Insight
Then for overall curriculum, I'd suggest:
1. start with basic machine learning (not neural networks) and in particular, read through the scikit-learn docs and watch a few tutorials on youtube. spend some time getting familiar with jupyter notebooks and pandas and tackle some real-world problems (kaggle is great or google around for datasets that excite you). Make sure you can solve regression, classification and clustering problems and understand how to measure the accuracy of your solution (understand things like precision, recall, mse, overfitting, train/test/validation splits)
2. Once you're comfortable with traditional machine learning, get stuck into neural networks by doing the fast.ai course. It's seriously good and will give you confidence in building near cutting-edge solutions to problems
3. Pick a specific problem area and watch a stanford course on it (e.g. cs231n for computer vision or cs224n for NLP)
4. Start reading papers. I recommend Mendeley to keep notes and organize them. The stanford courses will mention papers. Read those papers and the papers they cite.
5. Start trying out your own ideas and implementations.
While you do the above, supplement with:
* Talking Machines and O'Reilly Data Show podcasts
* Follow people like Richard Socher, Andrej Karpathy and other top researchers on Twitter
Good luck and enjoy!
The course in general lacks rigor, but I thought it was a very good first step.
Andrew Ng's Coursera course is probably good for some backgrounds. But if your background is as someone who has mostly been programming for the last few years, I feel that Andrew Ng's course has two big drawbacks:
1. It's not very hands-on or practical. You won't actually get the feeling of building anything for a while.
2. It's very math oriented. If the last time you took a math class for your CS degree was a few years ago, you run the risk of not really remembering the background material well.
I'd personally recommend doing two things in parallel, if your background is in programming with less math training:
1. Look for a very hands-on/practical course to try out some examples.
2. At the same time, start refreshing (or learning) some maths that you might not remember, specifically, probability and statistics. Then after, Linear Algebra and maybe calculus.
If you never took calculus it's probably going to be hard going, but almost all modern machine learning requires basic calculus.
I would really recommend going through the first part of the course about linear regression if you haven't encountered it before, it was really eye opening for me.
Again, this really depends on how mathematically competent you already are. I'm just basing this on how I felt coming to the course after having finished my degree about 10 years ago, therefore not really having most prob/statistics fresh in my mind.
It's the very first bit of the course, I think everyone who is interested should try learning it. If not it's fine, but I wouldn't want anyone to not even try to spend a few hours on it because someone on the internet said it would be too hard.
My worry is that people will be put off from the field of machine learning if, 3 lessons into Andrew Ng's course, they will see that they don't understand anything, and that it's not practical to boot.
So my advice (generally applicable) is to try a few different things, because different resources click for different people.
https://unsupervisedmethods.com/my-curated-list-of-ai-and-ma...
HN thread: https://news.ycombinator.com/item?id=14764700
http://blog.paralleldots.com/data-scientist/list-must-read-b...
Although this recommendation doesn't really fit the requirements of the poster, I think it is easy to reach first for modern, repackaged explanations and ignore the scientific literature. I think there is a great danger in that. Sometimes I think people are a bit scared to look at primary sources, so this is a great place to start if you are curious.
If you're any good, and have good results to show and talk about, yes, you could totally be employed.
If you show that you're extra willing to do all the heavy data preparation and labeling work yourself as well as the infrastructure that runs the models, you'll have an even easier time. Most people just want to play with models, and believe data preparation is "beneath" them, but that's actually where the meat is and where the success of the model is made or destroyed.