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How To Get Started With Machine Learning? | Two Minute Papers #51



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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42 thoughts on “How To Get Started With Machine Learning? | Two Minute Papers #51
  1. 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?

  2. 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."

  3. 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!!!

  4. 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?

  5. 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….

  6. 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?

  7. 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.

  8. 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.

  9. 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???

  10. explains basic concept of neural networks
    Me: I got this
    explains how to implement backpropogation
    Me: AAAAAAAAAAAAAAHHHHHHAAAAAAAAAAAAAHHHHHHHHHHH!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

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