4 ms·

Ask HN: Why there is no Codecademy for ML or AI?

28 points by allenleein 9y ago

bbennett36 9y ago
There is tons of online tutorials for these things. Kaggle has good tutorials with the test datasets
- allenleein 9y ago
  Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path. 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-models 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-algebra-fall-2011/index.htm 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/ From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
  - atarian 9y ago
    What does physics have to do with ML/AI?
    - kevinphy 9y ago
      "The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe" https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/ https://www.technologyreview.com/s/602344/the-extraordinary-...
  - JJarrard 9y ago
    Thank you!
jfaucett 9y ago
well dataquest.io is for data science but they seem to be going down that track.
scryder 9y ago
Codecademy's model tends to be very poor for topics that are sufficienty theoretically complex, as the turnaround time between exercise beginning and conclusion appears to be about 5 minutes for non-project work. Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent. Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.
coralreef 9y ago
Udacity has a program for AI, self driving car engineering, as well as a foundations program on deep learning .
eastWestMath 9y ago
It's called an M.Sc.
probinso 9y ago
Insight datascience
ssivark 9y ago
0. Doing ML requires an understanding of math. One gets less mileage from making magic incantations w/o understanding underlying implementation, compared with the mileage for front-end dev, as an example. 1. Any non-trivial ML will require significant amount of computational power (RAM/CPU/GPU). Who will subsidize that for participants? It's not sufficient to have a laptop that can run a browser. 2. Notice the proliferation of data science bootcamps. Once the syllabus/infrastructure becomes streamlined (and cheap), I would expect there to be more online offerings. That said, there might currently be a market need for a syllabus based on Kaggle or other freely available data sets, and free compute resources provided on cloud platforms.

28 points by allenleein 9y ago

bbennett36 9y ago

There is tons of online tutorials for these things. Kaggle has good tutorials with the test datasets

allenleein 9y ago
Yes, I did my research but there is no such interactive tutorial online like Treehouse or Codecademy. There are so many tutorials but none of it tells you the whole path. 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-models 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-algebra-fall-2011/index.htm 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/ From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.
- atarian 9y ago
  What does physics have to do with ML/AI?
  - kevinphy 9y ago
    "The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe" https://www.technologyreview.com/s/602344/the-extraordinary-link-between-deep-neural-networks-and-the-nature-of-the-universe/ https://www.technologyreview.com/s/602344/the-extraordinary-...
- JJarrard 9y ago
  Thank you!

jfaucett 9y ago

well dataquest.io is for data science but they seem to be going down that track.

scryder 9y ago

Codecademy's model tends to be very poor for topics that are sufficienty theoretically complex, as the turnaround time between exercise beginning and conclusion appears to be about 5 minutes for non-project work. Repetition/drilling for understanding and examination of knowledge by self-testing are wholly absent. Projects like "free code camp" can dodge this problem because the staff is working for free as an open source project, but it would take a lot more 5 minute segments to teach someone linear algebra, the theory behind any type of model, and the background information that is necessary to generate provably compelling insight than companies like Codeacademy seem interested in tackling.

coralreef 9y ago

Udacity has a program for AI, self driving car engineering, as well as a foundations program on deep learning .

eastWestMath 9y ago

It's called an M.Sc.

probinso 9y ago

Insight datascience

ssivark 9y ago

0. Doing ML requires an understanding of math. One gets less mileage from making magic incantations w/o understanding underlying implementation, compared with the mileage for front-end dev, as an example. 1. Any non-trivial ML will require significant amount of computational power (RAM/CPU/GPU). Who will subsidize that for participants? It's not sufficient to have a laptop that can run a browser. 2. Notice the proliferation of data science bootcamps. Once the syllabus/infrastructure becomes streamlined (and cheap), I would expect there to be more online offerings. That said, there might currently be a market need for a syllabus based on Kaggle or other freely available data sets, and free compute resources provided on cloud platforms.