Two Minute Papers
I get a lot of messages from you Fellow Scholars that you would like to get started in machine learning and are looking for materials. Below you find a ton of resources to get you started!
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The AI Revolution: The Road to Superintelligence on Wait But Why:
http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html
http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-2.html
Superintelligence by Nick Bostrom:
https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies
Courses:
Welch Labs – https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU
Andrew Ng on Coursera – https://class.coursera.org/ml-005/lecture
Andrew Ng (YouTube playlist) – https://www.youtube.com/playlist?list=PLA89DCFA6ADACE599
Nando de Freitas (UBC) – https://www.youtube.com/playlist?list=PLE6Wd9FR–Ecf_5nCbnSQMHqORpiChfJf
Nando de Freitas (Oxford) – https://www.youtube.com/playlist?list=PLE6Wd9FR–EfW8dtjAuPoTuPcqmOV53Fu
Nando de Freitas (more) – https://www.youtube.com/playlist?list=PLE6Wd9FR–EdyJ5lbFl8UuGjecvVw66F6
https://www.youtube.com/watch?v=PlhFWT7vAEw&list=PLjK8ddCbDMphIMSXn-w1IjyYpHU3DaUYw
One more at Caltech – https://work.caltech.edu/telecourse.html
Andrej Karpathy – https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC
UC Berkeley – https://www.youtube.com/channel/UCshmLD2MsyqAKBx8ctivb5Q/videos
Geoffrey Hinton – https://www.coursera.org/course/neuralnets
Machine Learning specialization at Coursera – https://www.coursera.org/specializations/machine-learning
MIT – http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/
Mathematicalmonk’s course: https://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA&index=0
“Pattern Recognition and Machine Learning” by Christoper Bishop:
http://research.microsoft.com/en-us/um/people/cmbishop/prml/
“Algorithms for Reinforcement Learning” by Csaba Szepesvári:
http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf
A great talk on deep learning libraries:
https://www.youtube.com/watch?v=Vf_-OkqbwPo&feature=youtu.be
Two great sources to check for new papers:
http://gitxiv.com/top
http://www.arxiv-sanity.com/top
Recent machine learning papers on the arXiv:
http://arxiv.org/list/stat.ML/recent
The Machine Learning Reddit:
http://www.reddit.com/r/MachineLearning/
One more great post on how to get started with machine learning:
https://www.quora.com/How-do-I-get-started-in-machine-learning-both-theory-and-programming/answer/Sebastian-Raschka-1
A great blog post on how to get started with Keras:
http://swanintelligence.com/first-steps-with-neural-nets-in-keras.html
A website with lots of intuitive articles on deep learning:
http://neuralnetworksanddeeplearning.com/
A free book on deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville:
http://www.deeplearningbook.org/
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Sunil Kim, Vinay S.
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Source
My favorite resource is this work-in-progress book: http://www.deeplearningbook.org/ the table of contents speaks for itself 🙂
I would also highly recommend Machine Learning Specialization at Coursera.
I'm doing it right now and it's the best ML course I've seen so far.
Thanks Karoly for your great approach at turning complex science into digestible and fun talks.
I would suggest as a good source for learning these MIT videos:
http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/
Thanks a lot of the video.
It is helpful…. ^_^
ML is not famous for math rigor, many papers lack it
I'm surprised no one's mentioning GIR.
sir thank you soo much for creating these amazing videos…..it really give you a headstart on what is it and how you can get gooing……thank you so much sir.
This is my favorite youtube channel!
Thanks for all your hard work. It is an awesome job of discriminating fast pace reseach fields. The ML episode há received quite a bit of attention. I would like to suggest a follow on literature review. From you many episodes, I noticed you fascination with ML. Could you do a compreensive Get Started episode on self improving algrithms? In the literature they seem to follow either the min-max games, image or 2-d inputs, or games with a perfect model previously know. How many ways have been architected to create self-improving models that attaing beyond human performance?
I just love it when you say "fellow scholars" cries in joy
it took me Two Minutes just to say his name Károly Zsolnai-Fehér
I always thought you said "fellow Scalars"… Would've been funny.
very cool. I appreciate that you know your audience
I can't get enough of your videos or this field. I've been inspired to take all the courses I avoided in college, just to have a minimum understanding this emerging tech. Thanks for distilling this in digestible chunks. Also, love the way you deliver "See you next time."
I thought that the MIT OCW lecture and recitation over neural nets helped me more than anything else to build my backpropogating network from scratch.
Is GA part of ML??
Machine learning must be accessible to the widest possible audience. We need as many people as possible teaching machines.
This channel is quickly becoming a favorite of mine. Thanks for all your hard work!
Start with Andrew ng's machine learning course from Coursera
Thank you so much for sharing!!! I wanted to buy some books on Amazon, but couldn't afford them, now I'm watching Andrew Ng's course! Again, thank you very much!!!
guys I'm beginner In ml please help me with study materials likes maths and all.. please
What should be my compteancy to start learning ?
Eh… no one mentioned the safety aspects? Diving in without any safe guards?
You definitely inspired me to start seriously learning machine learning!
Thank you Sir
thanks for the video but, calculation per seconds doesn't qualify computer to be as fast as human brain. Who defines how many calculations does the human brain do?
Are you from Malawi?
Osm
I understand most of the methods and I would want to learn the math behind machine Learning, but I can't get it installed with stuf f like Command prompts because they are cursed or something for me, commands don't exist even for copy paste etc….
Suddenly I began wondering myself what kind of hardware are people using for deep learning. I mean, are they using local infrastructures like servers or are using "the cloud" (datacenters or datacenter networks)? Is there any specialised component for AI like GPUs or something?
I'm a bit late to the party but I still would like to point out how helpfull this advice is, and how amazing it is how much and high quality learning material is avialable for free.
Do I see Gir?!
Can i use it in share market trading?
Wow! a clip less than 4 minutes with so much content followed by so many references to good stuff. That is a quality delivery. My deepest gratitude. Keep up the good work.
hey guys can anyone help me with this.iam currently pursuing undergraduate degree in bsc computing (bIt). how can i get started with machine lerning and ai after my completion of my undergraduation and want to get into machine lerning or ai and is there any masters courses related to ai with my degree???
Marvelous explaination thanku sir for the valuable info I had some doubts but this video cleared them…
Awesome videos thanks 2 minute s papers
Good one. Thanks
there is good series by mathematical monk here… https://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA&index=0
This was almost 4 minutes…Betrayal!!!
On a more serious note:
Thank you for all the sources! ☺
I have read the full article on waitbutwhy..
explains basic concept of neural networks
Me: I got this
explains how to implement backpropogation
Me: AAAAAAAAAAAAAAHHHHHHAAAAAAAAAAAAAHHHHHHHHHHH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!