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Ask HN: Best way to get started with AI?

• hackernewsacct 9y ago
  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.
  • ktta 9y ago
    Calculus: Thomas, Weir & Hass - Thomas' Calculus Linear Algebra (which is what you really need): Gilbert Strang - Linear Algebra and its applications These two are all you need, with which you'll get a solid base. Then you're good to go on your own. These two combined are about 4 semesters worth of work. But if you really focus, I think you can get them done in a little less than 6 months. If you want a 'just what I need' approach, Khan Academy.
  • dominotw 9y ago
    Spivak calculus. There is nothing like it. You will learn how to think in math, not just calculus.
    • anothertraveler 9y ago
      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!
  • _raoulcousins 9y ago
    For self-study, you don't necessarily need to follow the usual progression of math classes that start at calculus. It's more important to get comfortable with linear algebra than calculus, especially the way a lot of intro calculus courses focus on calculating integrals and derivatives. Maybe it's not the best message, but the worst grades in my math degree were in the intro calculus classes. I don't remember what intro linear algebra books I used, but my college uses this: https://www.math.ucdavis.edu/~linear/ https://www.math.ucdavis.edu/~linear/ (I took the class before this free textbook was developed).
  • cromulen 9y ago
    This Reddit post by a struggling Phd student has some great advice on catching up on maths. https://np.reddit.com/r/MachineLearning/comments/73n9pm/d_confession_as_an_ai_researcher_seeking_advice/dnrxui7/ https://np.reddit.com/r/MachineLearning/comments/73n9pm/d_co...
• minimaxir 9y ago
  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)
  • binarymax 9y ago
    Why does this annoy you? Some of the MOOCs are amazing and they certainly don't shortcut. I had many hard-learned lessons and realizations when going through the MOOCs I have taken. They also help in "knowing what you don't know"...which is essential in breaking out of ignorance.
  • hackathonguy 9y ago
    I've learned computer science in college, so I do have _some_ linear algebra background. I'm by no means an expert coder, but maybe 'intermediary' would have been a better description than 'novice'. :-)
  • dagw 9y ago
    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 9y ago
      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.
  • sillysaurus3 9y ago
    Elitism isn't too helpful. Determination counts for more than skill.
• binarymax 9y ago
  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 9y ago
    Thank you! Should I start with the Machine Learning one?
    • binarymax 9y ago
      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 9y ago
        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.
    • icc97 9y ago
      From my experience Andrew Ng wiped the floor with every other lecturer I've had. Both the ML and his new Deep Learning course. If the lecturers aren't very interesting Coursera can be as hard as any other lectures. I gave up on the Scala functional programming and disappointingly have stalled with Geoffrey Hinton's Neural Networks courses. But I really can't understate how good Andrew Ng is, he has a very relaxed manner and manages to make some very complex topics seem almost trivial. The worst of the mathematics is derivatives and matrix multiplication. You can even avoid matrix multiplication mostly in the ML course, but in his Deep Learning course he takes you through the 300x performance benefit you get from using NumPy and matrix multiplication vs loops.
• dmode 9y ago
  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 9y ago
    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 9y ago
      Did you find a job after?
• indescions_2017 9y ago
  Intro to AI https://www.udacity.com/course/intro-to-artificial-intelligence--cs271 https://www.udacity.com/course/intro-to-artificial-intellige... 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 ;)
• kmax12 9y ago
  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 9y ago
    Thanks for the featuretools project, it sounds really useful. Is it Python 2 only?
    • kmax12 9y ago
      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
• mceoin 9y ago
  fast.ai - it's free, and there's a low level of assumed knowledge from the outset.
  • wiremine 9y ago
    I'd second this. I'm not a "math person" so to speak, and they provide a great education that doesn't dip too deep into the math.
  • icc97 9y ago
    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/
  • bhollan 9y ago
    This. I have two engineering degrees and studied neural networks in college. He cuts down about 3 months worth of math to a couple of lines of code in such a way that makes it make sense AND is productive. It may just be that his teaching style matches my learning style really well, but I'm enjoying going through it.
• deepnotderp 9y ago
  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.
• hal9000xp 9y ago
  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
  • markatkinson 9y ago
    I came here to write a similar comment. Really make sure to watch the playlists in the correct order on the above YouTube channel.
  • skytreader 9y ago
    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=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x... 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 9y ago
      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.
• alexmuro 9y ago
  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-zLfQRF3EO8sYv https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-z... 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 9y ago
    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.
• mswen 9y ago
  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
• yters 9y ago
  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.
• aalleavitch 9y ago
  I've been going through this course: https://www.commonlounge.com/community/9dcdd386cc28446695305db00d2de532 https://www.commonlounge.com/community/9dcdd386cc28446695305... 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.
• projectramo 9y ago
  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 9y ago
    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 9y ago
      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.
    • projectramo 9y ago
      It would seem that many people have divergent definitions but I’ve always learned that AI was Artificial intelligence broadly construed (goal seeking, planning, rule based expertise, logic etc) while machine learning is the specific subset that is (roughly speaking) statistical (neural nets, regressions, svm and the like). Don’t have strong feelings about these definitions.
• wonder_bread 9y ago
  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-TensorFlow/dp/1491962291/ref=sr_1_2?ie=UTF8&qid=1510606358&sr=8-2&keywords=machine+learning+green https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-T...
• skadamat 9y ago
  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-fundamentals https://www.dataquest.io/course/machine-learning-fundamental...) 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).
• gncb 9y ago
  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
• smortaz 9y ago
  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 9y ago
    Interesting, didn't know about this, thanks for sharing
• bra-ket 9y ago
  Learn about human intelligence
• chestervonwinch 9y ago
  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.
• lottin 9y ago
  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 ;).
• austenallred 9y ago
  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 9y ago
    Would you do 20% for a salary of $100k or above? What percentage would you do for $100k?
    • austenallred 9y ago
      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.
• brandonhsiao 9y ago
  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).
• seriousssam 9y ago
  My friend and I wrote this guide called ML4Humans. https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 https://medium.com/machine-learning-for-humans/why-machine-l... 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
• leowoo91 9y ago
  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-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1 https://medium.com/technology-invention-and-more/how-to-buil...
• stonepresto 9y ago
  Berkeley has an free videos/slides, combined with exams, projects, and homework. Link:http: //ai.berkeley.edu/home.html
• jedanbik 9y ago
  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
• rwieruch 9y ago
  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-algorithm https://www.goodreads.com/book/show/24612233-the-master-algo... - [3] https://www.robinwieruch.de/linear-regression-gradient-descent-javascript/ https://www.robinwieruch.de/linear-regression-gradient-desce...
• aficionado 9y ago
  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/
• mooneater 9y ago
  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.
• scoot 9y ago
  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 9y ago
  Whenever you see a new term you don't know about watch/read 3 videos/article on it
• lee101 9y ago
  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.
• partycoder 9y ago
  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-Information-Statistics/dp/0387310738 https://www.amazon.com/Pattern-Recognition-Learning-Informat... Some people might prefer seeing things from another approach. http://pgm.stanford.edu/ http://pgm.stanford.edu/ Good luck.
• balp 9y ago
  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-python-introduction/ https://pythonprogramming.net/machine-learning-tutorial-pyth...
• deleted 9y ago
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• allenleein 9y ago
  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 9y ago
    This list is solid, and could keep you busy for a few years.
• godelmachine 9y ago
  In my humble opinion, there's no better way to start than with the classic book - " Artificial Intelligence : A Modern Approach " by Russel Norvig.
• andyjohnson0 9y ago
  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 9y ago
    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 9y ago
      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-Gibson/dp/1491914254 https://www.amazon.co.uk/Deep-Learning-Practitioners-Adam-Gi...
• ahamedirshad123 9y ago
  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/
• anothertraveler 9y ago
  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.
• erik14th 9y ago
  this one starts with simple stuff MIT 6.034 Artificial Intelligence: https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP63gFHB6xb-kVBiQHYe_4hSi https://www.youtube.com/watch?v=TjZBTDzGeGg&list=PLUl4u3cNGP...
• source99 9y ago
  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/