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Machine Learning Crash Course

1926 points by matant 9y ago

eddieplan9 9y ago
For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk. https://www.youtube.com/watch?v=vq2nnJ4g6N0 https://www.youtube.com/watch?v=vq2nnJ4g6N0
- null0pointer 9y ago
  +1 for this. Well worth the 2 hours.
- mlevental 9y ago
  the deep nets and conv nets stuff was excellent. i wish the explanation of rnns was a little better.
chris_va 9y ago
Link to the exercises: https://developers.google.com/machine-learning/crash-course/exercises https://developers.google.com/machine-learning/crash-course/...
neves 9y ago
Which languages and libraries does it use? Is it based in Google libs, like Tensor Flow?
- otoburb 9y ago
  Clicking through to the homepage, the full title is: "Machine Learning Crash Course with TensorFlow APIs".
zengid 9y ago
I wonder if they are doing this to complete with course.fast.ai
- zeroxfe 9y ago
  This has been an internal course at Google for many years.
  - chishaku 9y ago
    I think that helps to prove the point.
- bllguo 9y ago
  IMO teaching people ML is good in general for Google. 1. it spreads the use of tensorflow, 2. it increases not only tensorflow, but also Google's mindshare, 3. it trains people that may become future Google employees, and/or serves as a useful resource for existing employees
  - jxub 9y ago
    Also 4) will increment the usage of TPU's on Google Cloud Platform and subsequently the revenue of their cloud offerings.
kidfiji 9y ago
I've been itching to learn a bit about the industry and to be able to create & train ML models myself; I'm glad Google decided to put out a course where I wouldn't have to worry about the quality of instruction.
throwaway84742 9y ago
Unless you have already invested a lot of time into learning (and building on top of) TF, I would advise to pick up PyTorch. It’s much easier to learn and use (imperative!), and has higher performance on common workloads.
- jazoom 9y ago
  Except there aren't many good resources to learn it and the documentation isn't very good. Hopefully this will improve soon.
  - throwaway84742 9y ago
    On the positive side with PyTorch you don’t need nearly as much documentation as you would with TF. TF in general feels like it’s fighting you every step of the way. There’s a lot of cognitive overhead. Not so with PyTorch. Everything is straightforward, and can be run/examined in ipython.
mrdmnd 9y ago
As someone who just did this internally: Do it. It's worth your time. Very well paced exercises, and it walks you through the flow quite nicely.
- tostitos1979 9y ago
  I went through the first a couple of topics. It seemed very disjointed. Different people presenting, different exercises. Was it like this internally? Or is this heavily "annotated"?
minimaxir 9y ago
Looking through the topics covered, the standard AI-course caveats (https://news.ycombinator.com/item?id=16247629 https://news.ycombinator.com/item?id=16247629) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. However, contrary to the thought pieces that tend to pop up, taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats applying the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process. Reviewing the Keras documentation (https://keras.io https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion. (Of course, Keras is now a part of TensorFlow, so there's a neat Google vertical intergration with this crash course!)
- ariwilson 9y ago
  Isn't this meant to be an introduction? I'm not sure who comes out of a crash course assuming they're an expert.
- jorgemf 9y ago
  > "you can't use AI/ML unless you have a PhD/5 years research experience" This is not true since a few years ago. But the fact that you can use it doesn't mean you understand what is happening and why it works in development but not in production. Everybody can copy a jupyter notebook and train a TensorFlow model in ImageNet. Now go to a new domain with very few information like 3D models and create a new network to be trained in that dataset. How many people that can train ImageNet can do the latter? Even inside deep learning experts in image classification fail in reinforcement learning domains and need a couple of years to be completely productive.
- acconrad 9y ago
  > The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. So what are they hoping to achieve with this course? I'm genuinely asking because part of me wants to take the course, but another part of me feels like what's the point if, even through many additional courses to build up a skill set, Google wouldn't hire you as an ML engineer unless you basically start your career back to a junior engineer but in machine learning at another company.
kmax12 9y ago
Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/representation/feature-engineering https://developers.google.com/machine-learning/crash-course/.... If anyone is looking to get more indepth, I work on an open source python library for automated feature engineering called Featuretools https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/. It can help when your data is more complex such as when it comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos.
- minimaxir 9y ago
  Although I'm normally skeptical of AI/ML courses, that section on feature engineering do's-and-do-nots is new and surprisingly under-discussed. It's very useful even outside of AI/ML.
  - kmax12 9y ago
    I agree. I expect that as companies increase their focus on finding practical applications of ML / AI, the topic will start to get more attention in these tutorials, as well as from researchers. Right now, too many people assume you already have a feature matrix, which is rarely the case when working on real world problems.
- cjalmeida 9y ago
  OTOH, automating feature engineering is a thing. There are papers on using unsupervised methods to do that. The 1st place in Kaggle's Porto Seguro competition trained an Autoencoder on raw data to extract features.
- fnl 9y ago
  Your comment got me interested in this course. However, all I could find about feature engineering there is what you linked to, directly. Given that entire scientific careers, books, and conferences are built around the topic of feature engineering, and at least IMO good ML tools live or die with good feature engineering (in its broadest sense, for you deep learning fanatics :-)) that doesn't seem like more than the bare minimum I'd expect from any ML "crash-course" that is to be taken serious (and I wouldn't expect an ounce less from Google... :-)). Am I missing something, maybe? In any case, nice work of your own, and thanks for sharing it!
ariwilson 9y ago
Having done this course 6 months ago, this is a fantastic introduction to the major concerns of practical machine learning.
maximumkek 9y ago
I have been programing html css and JS for a whole year and I cannot understand any of this pseudo intellectual mumbo jumbo
graycat 9y ago
In the course, in lecture "Reducing Loss: Gradient Descent" is "Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges." The first sentence is flatly wrong: E.g., for positive integer n and the set of real numbers R, function f: R^n --> R where for all x in R^n f(x) = 0, f is convex, concave, and linear, and for all x in R^n x is a minimum and a maximum of f. Can there be uncountably infinitely many alternative minima for the Google ML problems? Yes, e.g., just enter one of the independent variables twice. The second sentence is nonsense. Grotesque, outrageous incompetence!!!! It has long been known that minimizing a convex function, even a differentiable convex function, with just gradient descent can be just horribly inefficient. A LOT is known about how to do much better than just gradient descent. E.g., there is Newton iteration (right, that Newton, hundreds of years ago) and quasi-Newton. And there's more. Why so inefficient? Well, draw a picture like Google did except use just two independent variables instead of just the one in the Google picture. Then see that the resulting, convex "bowl" can be like a long, narrow boat with a very gentle slope in one direction and a very steep slope in an orthogonal direction. Yes the cross section of the bowl can be, first cut, an ellipse with one short axis and one long one. Sure, the axes are eigenvectors, etc. and the ellipse is part of a local quadratic approximation. Well, gradient descent keeps going back and forth nearly parallel to the short axis of the ellipse and making nearly no progress on the long axis. People have known this and known good things to do about it for, uh, at least half a century. For the Google ML problems, might (1) tweak Newton iteration to improve the rate of convergence to a minimum or (2) at each iteration don't get a gradient descent but get a supporting hyperplane of the epigraph of the convex function, as the iterations proceed, accumulate these hyperplanes, notice that they lead to an approximation of the full epigraph, and use linear programming or some tweak of that to minimize the hyperplane approximation to the convex function -- there is much more that can be said here. By the way, the convex function for that ML problem is quite special, e.g., quadratic. A lot has long been known and well polished in regression and classification, e.g., with TeX markup: N.\ R.\ Draper and H.\ Smith, {\it Applied Regression Analysis,\/} John Wiley and Sons, New York, 1968.\ \ Leo Breiman, Jerome H.\ Friedman, Richard A.\ Olshen, Charles J.\ Stone, {\it Classification and Regression Trees,\/} ISBN 0-534-98054-6, Wadsworth \& Brooks/Cole, Pacific Grove, California, 1984.\ \ C.\ Radhakrishna Rao, {\it Linear Statistical Inference and Its Applications:\ \ Second Edition,\/} ISBN 0-471-70823-2, John Wiley and Sons, New York, 1967.\ \ A good start on convexity is Wendell H.\ Fleming, {\it Functions of Several Variables,\/} Addison-Wesley, Reading, Massachusetts, 1965.\ \ Total, fun, dessert ice cream on convexity is Jensen's inequality; right away can use it to prove a lot of classic inequalities. Gee, look on the upside!!!! From this sample, the claims of machine learning (ML) revolutionizing the economy are nonsense!!!! And for startups, don't much have to worry about serious competition from Google!!!!
austenallred 9y ago
Shameless plug: Lambda School (YC S17) is also putting on a free Machine Learning crash course (we call it a mini bootcamp), followed by an optional 6-12 month course that you pay for once you get a job in data science (it’s free until then, and always free if you don’t get a job in ML). https://lambdaschool.com/machine-learning-bootcamp/ https://lambdaschool.com/machine-learning-bootcamp/
- eorge_g 9y ago
  is there a more fleshed out outline for what will be covered here? sounds interesting
juanmirocks 9y ago
The Google AI courses are also good for old seasoned ML practitioners who want to learn more about more recent deep learning techniques
Abundnce10 9y ago
I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the various parts of the ingredient text.
- maffydub 9y ago
  It's not very sexy, but I think you might find it easier and more robust just to use an NLP library. I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results. Good luck!
  - Abundnce10 9y ago
    I think you're right. Did you happen to open-source your code from the hackathon? I'd love to take a look at your approach if you don't mind.
    - maffydub 9y ago
      Sorry, I normally would but one of the other team members is considering taking the hack forward and wanted to keep it closed for now. (It's hard to see how much competitive advantage he'd have from 48 hours of very-hacked-together code, but so few hackathon projects get taken forward that I didn't want to discourage him!) The approach was to tokenize the input and then do basic pattern-matching on it, with separate dictionaries of quantity units (e.g. cup, oz, pound) ingredients, processing words (e.g. "chopped") and throw-away words (e.g. "of"). In fact, possibly the most complicated part was parsing "2.5", "2 and a half" and "2½" all to the same thing.
- telchar 9y ago
  This seems relevant: https://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/ https://open.blogs.nytimes.com/2015/04/09/extracting-structu...
qwerty456127 9y ago
Cool! Does anybody also know a good blockchain crash course of similar kind so one could grok all the major buzzwords of today?
- lanewinfield 9y ago
  I loved this one a lot: https://anders.com/blockchain/ https://anders.com/blockchain/
  - abhishekjha 9y ago
    This is what got me a proper intuition for why a blockchain can be useful.
redditmigrant 9y ago
As someone who is trying to learn ML, all the courses available are hugely helpful. One thing I wish I had easy access to is the process that someone goes through while trying to build a model on a real dataset. Specifically following questions are the ones I struggle with: 1. How did you figure out what features would be useful? 2. How did you figure out what algorithm(s) are appropriate? 3. how and why did you massage the data in a specific way?
- disgruntledphd2 9y ago
  If you are willing to do the work, Frank Harrell's Regression Modeling Strategies is a pretty good introduction to a lot of this. It's written for a very different set of problems than typical ML, but it has lots of really good advice for practical problems in data analysis and prediction (which is another term for ML). Mostly people learn this stuff by experience. Find a dataset, choose a predictor, filter, clean and massage your data till you get better metrics/understanding (preferably both). Rinse, repeat on many different datasets and problems, and you'll know how to do this.
- bkanber 9y ago
  > How did you figure out what features would be useful? There are various feature engineering and feature extraction techniques. Filter methods, wrapper methods, and embedded methods. Principle component analysis, autoencoding, variance analysis, linear discriminant analysis, Gini index, genetic algorithms, etc -- the feature selection process will depend on the dataset, the problem domain, the analysis algorithm you ultimately use, etc. > How did you figure out what algorithm(s) are appropriate? Also depends on the problem domain. Discrete or continuous data? Categorical features, numeric features, features as bitmasks. Do you need a probabilistic outcome? Etc. Generally you start with the easiest algorithms in your toolbox to see how viable they are. For a classification task I'll almost always start with a naive Bayes classifier (if the data allows) and/or a random forest and see how they perform. If the problem domain is highly non-linear you might start with a support vector or kernel method. Neural network is a last resort for me, as I find most classification problems can be solved to a high accuracy much more simply. > how and why did you massage the data in a specific way? This relates back to #1 -- you should only massage data based on what your feature engineering tells you to do. Sometimes you might want to remove outliers or clean up the training data, but only if the outliers really should be removed from consideration entirely.
suyash 9y ago
Is there something like this for Java programmers?
- rripken 9y ago
  I think Weka gets used in the java world. https://www.cs.waikato.ac.nz/ml/weka/ https://www.cs.waikato.ac.nz/ml/weka/
- lovelearning 9y ago
  ML concepts are independent of programming language. I suggest not to let language preferences stand in the way of listening to lectures and understanding the subject. For implementing exercises using Java, you have a bunch of good options: 1) The most direct equivalent to Pandas+Tensorflow I can think of is DL4J. They have a good comprehensive set of concept and implementation tutorials [1]. 2) TF APIs have a Java port and can be used from java desktop and console applications [2]. So a second but slightly more difficult option is using TF Java port + Spark APIs. [1]: https://deeplearning4j.org/documentation https://deeplearning4j.org/documentation [2]: https://www.tensorflow.org/install/install_java https://www.tensorflow.org/install/install_java
bjourne 9y ago
I want to ask people who know ML well if the hype is warranted? Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?
- bkanber 9y ago
  The hype is and isn't warranted. ML is a much broader field than just neural networks. The hype for ML, in general, I think is warranted. We hit an inflection point when AWS launched and scalable processing power became cheap. It became cheap to process tons of data and generate insights. I don't have hard numbers on this, but probably 90-95% of machine learning used in practice is NOT neural networks, and have accuracies in the 90%+ arena. So ML in general -- sure, hype warranted. Neural networks are the new hot topic, and the hype isn't fully warranted yet. TensorFlow made them very popular in the developer community; this is a good thing because it's spurring more investment and research in ANNs. But for any given problem, odds are that a neural network is not the best (ie, most accurate or cheapest) way to solve it. Neural networks do have specific problem domains where they are the state of the art, but for most other problem domains there exists a better solution. So I'd say that neural networks are a little over-hyped right now, but with a new generation of developers learning about and experimenting with ANNs, that will change in a few years. I think we're about to see an explosion of ANN usefulness over the next few years. TLDR: ML is very useful but is more than neural networks; neural networks need a little more progress to catch up to the tensorflow hype.
- ageitgey 9y ago
  Computers are automation tools that increase human efficiency by doing the grunt work for you - but they are limited to automating the tasks that can be captured as a set of rules in code. When we figure a new way to model more complex tasks in code, a whole new set of things can be automated. Here's a concrete example: Before spreadsheets existed, there used to be legions of accountants who created complex ledgers on paper and added up all numbers to track how a business was doing. You'd literally mail off your sales numbers to an accounting team somewhere and wait three days to get the latest report generated and sent back. Sure they had calculators to add numbers, but the computers of the day didn't understand how those numbers related to each other. The human still had to do most of the work to create the reports. The big idea of spreadsheets was to make the computer manage the more complex task of knowing how different numbers in a report related to each other. It made most ledger tasks totally automatic once the initial report was defined. Now a single accountant could do the work of the entire accounting team - and more accurately and in less time! There were stories of the first spreadsheet testers having to delay mailing back their financial reports by a few days because their clients would be suspicious if they mailed them back too fast. Nearly overnight accounting got a lot more efficient and companies made more money. T"What If" modeling that used to be too slow and cost prohibitive to do was now it was quick and easy. Companies could plan more intelligently. The spreadsheet was a true game changer. This same pattern happens every time the bar is raised on the complexity of what can be automated and Machine Learning raises the bar one giant notch. Previously we were limited to automating tasks that a smart coder could describe as discrete steps in code. But with ML, the computer can figure out it's own rules just by looking at data. That means in many cases you can solve very hard problems just by collecting a lot of data. Lots and lots of things that used to be done by large groups of people will now be able to be done with a single computer. In that sense, ML is a total game changer. Don't focus on the specific applications thus far. Focus on the idea that all kinds of tasks that used to require humans can now be automated with a little bit of applied ML. The opportunities are literally everywhere. In a few years, ML won't be some esoteric technique used by a few people. It will be a core skill that everyone uses or touches in some way. It's going to creep into everything everywhere because it's just so darn useful.
rtfs 9y ago
Thanks Google! Now I know that I am a ML guy, as an economist and econometrician. Yes, we shoot this on all kind of stuff, though with a clear business acumen or economic policy thinking.
andyjohnson0 9y ago
This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future. Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fundamentals without an API getting in the way, and developing some good intuition. Obviously frameworks are the way to go for production ML work, but I wonder whether ML people here think this bottom-up approach is advisable or could it be misleading when I move on to Keras/TensorFlow/whatever? [1] https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning Edit: brevity
- bkanber 9y ago
  I teach ML and am currently writing my 2nd book on it. I always advocate learning the fundamentals. Machine learning is math, and neural networks in particular rely on linear algebra and vector calculus. (You can build a NN without using linear algebra directly, likely it'll be slower and besides, the concept still relies on linear algebra). Frameworks abstract away a lot of the mathiness, which is a net good for society (ie, exposing lots of developers to neural networks), but I consider that a net-negative for the individual developer. When working on anything but trivial toy problems, you should make sure you understand your problem domain and implementation thoroughly. Is the activation function you've chosen ideal for your problem domain? If not, choose a better one. If no better one exists, you can invent it; but you'll also need to know how to design the backpropagation algorithm for that new activation function (which requires some vector calculus). Learning the math, as you have, helps you tune your algorithm based on actual knowledge rather than guesswork. I don't think it will be misleading when you move on to a framework. The frameworks are built on the same math. That said -- if all you're looking to do is play around, then you don't need the math as much.
  - andyjohnson0 9y ago
    Thanks for taking the time to write such a comprehensive reply - much appreciated. "ML is maths" is something that I'm getting used to now. I do have some real uses in mind for what I'm learning' both in my job and some side projects, particularly image feature recognition, and I'm looking forward so seeing how it all works in out. Thanks again!
zitterbewegung 9y ago
I like this move from google. Sure it is targeted for you to use Tensorflow but more courseware and MOOCs help everyone. I love doing self study and Tensorflow's tutorials are top notch. Since I can also use Tensorflow on my own hardware and anywhere else I really love better docs and MOOCs in general. What I really want to do is understand enough Tensorflow to reproduce other people's experiments in their papers on github and I think this would be one of the best ways to do this. Of course, this may eat into a bunch of companies that have paid programs for ML but its Google's prerogative to make ML cheaper and easier to deploy and learn so I am all for that.
bpesquet 9y ago
The choice of TensorFlow is a bit disappointing for a beginner-focused course which looks really solid otherwise. Business seems to have gotten priority over pedagogy in that case. I see TensorFlow as the Angular of machine learning: first on the market, powerful but unwiedly. Like Angular, it will ultimately get superseded by tools with a nicer API (scikit-learn, Keras) or more versatility (PyTorch). Like Angular, it's probably not the best choice for a beginner to invest time into.
- dragandj 9y ago
  Add to that that TensorFlow was practically a latecomer, not the first to market.
ivan_ah 9y ago
I love the prework section: https://developers.google.com/machine-learning/crash-course/prereqs-and-prework https://developers.google.com/machine-learning/crash-course/... It's a very good mix of topic and skills that I think everyone should learn, even if not directly planning to do ML or DL. If y'all are looking for a compact (and inexpensive) textbook on linear algebra that comes with all prerequisites you can check out: https://gum.co/noBSLA https://gum.co/noBSLA (disclaimer: I wrotes it)
DiogoMCampos 9y ago
How does this compare to the CS229 lectures by Andrew Ng? (the recorded lectures, not the MOOC)
- manav 9y ago
  CS229 @ Stanford is very math/proof intensive. This might be somewhat similar to CS221 or the Coursera course.
loreteye 9y ago
My friend partner was out dating his friend at work and he needed an hacker to help spy her device. He got (Roccoshadow (at) gmx (.) com) to help spy her iPhone,facebook,email,whatsapp,calls,skype and others.They literally helped him and now he have her whatsapp Spyed without her noticing.
rohitpaulk 9y ago
I've compiled this into a Todoist template you can import - it's got links to each module + times. To preview: http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google-ml-crash-course http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google-ml-c... To import directly: https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd1aspxi4rjqbaz.cloudfront.net%2F24fc6edd6dff480670528e9f842cb494_Google%2520ML%2520crash%2520course.csv https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd...
msaharia 9y ago
On a sidenote, can someone talk about what kind of tools are being used to integrate the subtitles and scrolling behavior over a youtube video in this course? Is there an OS implementation?
aabajian 9y ago
While I like the idea, in principle, that you don't need a CS education to use AI/ML, I doubt it. Here's a problem that cropped up today: Our instance ran out of hard drive space on a training set of ~400,000 images. The individual images were only 375 GB, but took up 1.5T when converted to Numpy matrices. Why? The arrays were converted to standard int arrays (32-bit x 3 channels) when they could've fit into short (8-bit x 3 channels). Each image was 4x as large as it needed to be. You can certainly use high-level ML tools (like Keras), but it takes a great deal of work to wrangle your data into a usable format, and even more knowledge to debug an ineffective network.
blueside 9y ago
we recommend that students meet the following prerequisites:Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms Any book suggestions to getting up to speed in this area?
- ropable 9y ago
  Khan Academy probably has this covered.
  - toomuchtodo 9y ago
    A shame Google doesn’t just link to the Khan Academy course.
    - earth2mars 9y ago
      not true. they do have khan academy link where applicable. for example for the algebra ones. check below link https://developers.google.com/machine-learning/crash-course/prereqs-and-prework#prerequisites https://developers.google.com/machine-learning/crash-course/...
- megaman22 9y ago
  I always liked the Saxon books[1], since they involved so much spaced repetition if you did the problem sets that it beat the symbolic manipulation into your long-term memory. [1] http://amzn.to/2FH3bXL http://amzn.to/2FH3bXL
- deleted 9y ago
  [deleted]
- jpamata 9y ago
  If you're looking for books, have a look at Schaum's Outline of Precalculus [0]. Khan Academy [1] is also good and there's this MOOC on coursera called Data Science Math Skills [2]. [0] https://www.amazon.com/Schaums-Outline-Precalculus-3rd-Problems/dp/0071795596 https://www.amazon.com/Schaums-Outline-Precalculus-3rd-Probl... [1] https://www.khanacademy.org/math https://www.khanacademy.org/math [2] https://www.coursera.org/learn/datasciencemathskills https://www.coursera.org/learn/datasciencemathskills
gcb0 9y ago
this site is so broken on firefox mobile they should be embarrassed.
ak_yo 9y ago
Cool tutorial, but I'm not entirely sure what makes this ML -- aside from neural nets, this is more or less the material you'd encounter in a basic applied statistics or regression analysis course, minus material on estimating uncertainty, modeling survival or time-series data, and causal inference. I suspect you'd benefit more from a 50 minute tutorial on those than neural nets.
earth2mars 9y ago
They do have pre-reqs training material with reference links. https://developers.google.com/machine-learning/crash-course/prereqs-and-prework#prerequisites https://developers.google.com/machine-learning/crash-course/...
noahha 9y ago
serving videos from youtube without alternatives doesn't make it accessible. proxying for identified videos esspecially videos from this crash course would be useful.
- Mashimo 9y ago
  >serving videos from youtube without alternatives doesn't make it accessible. Why is that?
deleted 9y ago
[deleted]
Animats 9y ago
Can't view. Requires a Google account.
- Tepix 9y ago
  Have you tried to create one?
deleted 9y ago
[deleted]
DeepWorker 9y ago
The ulterior motive behind this is to increase usage of Google Cloud (according to this answer on Quora: https://www.quora.com/Why-did-Google-release-their-machine-learning-crash-course/answer/Carlos-Matias-La-Borde?share=00711d71&srid=XvlZ https://www.quora.com/Why-did-Google-release-their-machine-l...)

1926 points by matant 9y ago

For those interested in a deeper dive to just deep learning, "Tensorflow and deep learning - without a PhD" is really good, and covers a lot of material in a single 2hr talk. https://www.youtube.com/watch?v=vq2nnJ4g6N0 https://www.youtube.com/watch?v=vq2nnJ4g6N0

null0pointer 9y ago
+1 for this. Well worth the 2 hours.
mlevental 9y ago
the deep nets and conv nets stuff was excellent. i wish the explanation of rnns was a little better.

chris_va 9y ago

Link to the exercises: https://developers.google.com/machine-learning/crash-course/exercises https://developers.google.com/machine-learning/crash-course/...

neves 9y ago

Which languages and libraries does it use? Is it based in Google libs, like Tensor Flow?

otoburb 9y ago
Clicking through to the homepage, the full title is: "Machine Learning Crash Course with TensorFlow APIs".

zengid 9y ago

I wonder if they are doing this to complete with course.fast.ai

zeroxfe 9y ago
This has been an internal course at Google for many years.
- chishaku 9y ago
  I think that helps to prove the point.
bllguo 9y ago
IMO teaching people ML is good in general for Google. 1. it spreads the use of tensorflow, 2. it increases not only tensorflow, but also Google's mindshare, 3. it trains people that may become future Google employees, and/or serves as a useful resource for existing employees
- jxub 9y ago
  Also 4) will increment the usage of TPU's on Google Cloud Platform and subsequently the revenue of their cloud offerings.

kidfiji 9y ago

I've been itching to learn a bit about the industry and to be able to create & train ML models myself; I'm glad Google decided to put out a course where I wouldn't have to worry about the quality of instruction.

throwaway84742 9y ago

Unless you have already invested a lot of time into learning (and building on top of) TF, I would advise to pick up PyTorch. It’s much easier to learn and use (imperative!), and has higher performance on common workloads.

jazoom 9y ago
Except there aren't many good resources to learn it and the documentation isn't very good. Hopefully this will improve soon.
- throwaway84742 9y ago
  On the positive side with PyTorch you don’t need nearly as much documentation as you would with TF. TF in general feels like it’s fighting you every step of the way. There’s a lot of cognitive overhead. Not so with PyTorch. Everything is straightforward, and can be run/examined in ipython.

mrdmnd 9y ago

As someone who just did this internally: Do it. It's worth your time. Very well paced exercises, and it walks you through the flow quite nicely.

tostitos1979 9y ago
I went through the first a couple of topics. It seemed very disjointed. Different people presenting, different exercises. Was it like this internally? Or is this heavily "annotated"?

minimaxir 9y ago

Looking through the topics covered, the standard AI-course caveats (https://news.ycombinator.com/item?id=16247629 https://news.ycombinator.com/item?id=16247629) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. However, contrary to the thought pieces that tend to pop up, taking and passing a crash course doesn't mean you'll be an expert in the field (and this applies for most MOOCs, honestly). They're very good for learning an overview of the technology, but nothing beats applying the tools on a real-world, noisy dataset, and solving the inevitable little problems that crop up during the process. Reviewing the Keras documentation (https://keras.io https://keras.io) and examples (https://github.com/keras-team/keras/tree/master/examples https://github.com/keras-team/keras/tree/master/examples) are honestly much better teachers of AI/ML than any MOOC, in my opinion. (Of course, Keras is now a part of TensorFlow, so there's a neat Google vertical intergration with this crash course!)

ariwilson 9y ago
Isn't this meant to be an introduction? I'm not sure who comes out of a crash course assuming they're an expert.
jorgemf 9y ago
> "you can't use AI/ML unless you have a PhD/5 years research experience" This is not true since a few years ago. But the fact that you can use it doesn't mean you understand what is happening and why it works in development but not in production. Everybody can copy a jupyter notebook and train a TensorFlow model in ImageNet. Now go to a new domain with very few information like 3D models and create a new network to be trained in that dataset. How many people that can train ImageNet can do the latter? Even inside deep learning experts in image classification fail in reinforcement learning domains and need a couple of years to be completely productive.
acconrad 9y ago
> The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. So what are they hoping to achieve with this course? I'm genuinely asking because part of me wants to take the course, but another part of me feels like what's the point if, even through many additional courses to build up a skill set, Google wouldn't hire you as an ML engineer unless you basically start your career back to a junior engineer but in machine learning at another company.

kmax12 9y ago

Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/representation/feature-engineering https://developers.google.com/machine-learning/crash-course/.... If anyone is looking to get more indepth, I work on an open source python library for automated feature engineering called Featuretools https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/. It can help when your data is more complex such as when it comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos.

minimaxir 9y ago
Although I'm normally skeptical of AI/ML courses, that section on feature engineering do's-and-do-nots is new and surprisingly under-discussed. It's very useful even outside of AI/ML.
- kmax12 9y ago
  I agree. I expect that as companies increase their focus on finding practical applications of ML / AI, the topic will start to get more attention in these tutorials, as well as from researchers. Right now, too many people assume you already have a feature matrix, which is rarely the case when working on real world problems.
cjalmeida 9y ago
OTOH, automating feature engineering is a thing. There are papers on using unsupervised methods to do that. The 1st place in Kaggle's Porto Seguro competition trained an Autoencoder on raw data to extract features.
fnl 9y ago
Your comment got me interested in this course. However, all I could find about feature engineering there is what you linked to, directly. Given that entire scientific careers, books, and conferences are built around the topic of feature engineering, and at least IMO good ML tools live or die with good feature engineering (in its broadest sense, for you deep learning fanatics :-)) that doesn't seem like more than the bare minimum I'd expect from any ML "crash-course" that is to be taken serious (and I wouldn't expect an ounce less from Google... :-)). Am I missing something, maybe? In any case, nice work of your own, and thanks for sharing it!

ariwilson 9y ago

Having done this course 6 months ago, this is a fantastic introduction to the major concerns of practical machine learning.

maximumkek 9y ago

I have been programing html css and JS for a whole year and I cannot understand any of this pseudo intellectual mumbo jumbo

graycat 9y ago

In the course, in lecture "Reducing Loss: Gradient Descent" is "Convex problems have only one minimum; that is, only one place where the slope is exactly 0. That minimum is where the loss function converges." The first sentence is flatly wrong: E.g., for positive integer n and the set of real numbers R, function f: R^n --> R where for all x in R^n f(x) = 0, f is convex, concave, and linear, and for all x in R^n x is a minimum and a maximum of f. Can there be uncountably infinitely many alternative minima for the Google ML problems? Yes, e.g., just enter one of the independent variables twice. The second sentence is nonsense. Grotesque, outrageous incompetence!!!! It has long been known that minimizing a convex function, even a differentiable convex function, with just gradient descent can be just horribly inefficient. A LOT is known about how to do much better than just gradient descent. E.g., there is Newton iteration (right, that Newton, hundreds of years ago) and quasi-Newton. And there's more. Why so inefficient? Well, draw a picture like Google did except use just two independent variables instead of just the one in the Google picture. Then see that the resulting, convex "bowl" can be like a long, narrow boat with a very gentle slope in one direction and a very steep slope in an orthogonal direction. Yes the cross section of the bowl can be, first cut, an ellipse with one short axis and one long one. Sure, the axes are eigenvectors, etc. and the ellipse is part of a local quadratic approximation. Well, gradient descent keeps going back and forth nearly parallel to the short axis of the ellipse and making nearly no progress on the long axis. People have known this and known good things to do about it for, uh, at least half a century. For the Google ML problems, might (1) tweak Newton iteration to improve the rate of convergence to a minimum or (2) at each iteration don't get a gradient descent but get a supporting hyperplane of the epigraph of the convex function, as the iterations proceed, accumulate these hyperplanes, notice that they lead to an approximation of the full epigraph, and use linear programming or some tweak of that to minimize the hyperplane approximation to the convex function -- there is much more that can be said here. By the way, the convex function for that ML problem is quite special, e.g., quadratic. A lot has long been known and well polished in regression and classification, e.g., with TeX markup: N.\ R.\ Draper and H.\ Smith, {\it Applied Regression Analysis,\/} John Wiley and Sons, New York, 1968.\ \ Leo Breiman, Jerome H.\ Friedman, Richard A.\ Olshen, Charles J.\ Stone, {\it Classification and Regression Trees,\/} ISBN 0-534-98054-6, Wadsworth \& Brooks/Cole, Pacific Grove, California, 1984.\ \ C.\ Radhakrishna Rao, {\it Linear Statistical Inference and Its Applications:\ \ Second Edition,\/} ISBN 0-471-70823-2, John Wiley and Sons, New York, 1967.\ \ A good start on convexity is Wendell H.\ Fleming, {\it Functions of Several Variables,\/} Addison-Wesley, Reading, Massachusetts, 1965.\ \ Total, fun, dessert ice cream on convexity is Jensen's inequality; right away can use it to prove a lot of classic inequalities. Gee, look on the upside!!!! From this sample, the claims of machine learning (ML) revolutionizing the economy are nonsense!!!! And for startups, don't much have to worry about serious competition from Google!!!!

austenallred 9y ago

Shameless plug: Lambda School (YC S17) is also putting on a free Machine Learning crash course (we call it a mini bootcamp), followed by an optional 6-12 month course that you pay for once you get a job in data science (it’s free until then, and always free if you don’t get a job in ML). https://lambdaschool.com/machine-learning-bootcamp/ https://lambdaschool.com/machine-learning-bootcamp/

eorge_g 9y ago
is there a more fleshed out outline for what will be covered here? sounds interesting

juanmirocks 9y ago

The Google AI courses are also good for old seasoned ML practitioners who want to learn more about more recent deep learning techniques

Abundnce10 9y ago

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the various parts of the ingredient text.

maffydub 9y ago
It's not very sexy, but I think you might find it easier and more robust just to use an NLP library. I built something similar (albeit for a relatively limited database of recipes) for a hackathon a couple of weeks back. I didn't even use a proper NLP library, just some simple hand-rolled pattern-matching, and got pretty good results. Good luck!
- Abundnce10 9y ago
  I think you're right. Did you happen to open-source your code from the hackathon? I'd love to take a look at your approach if you don't mind.
  - maffydub 9y ago
    Sorry, I normally would but one of the other team members is considering taking the hack forward and wanted to keep it closed for now. (It's hard to see how much competitive advantage he'd have from 48 hours of very-hacked-together code, but so few hackathon projects get taken forward that I didn't want to discourage him!) The approach was to tokenize the input and then do basic pattern-matching on it, with separate dictionaries of quantity units (e.g. cup, oz, pound) ingredients, processing words (e.g. "chopped") and throw-away words (e.g. "of"). In fact, possibly the most complicated part was parsing "2.5", "2 and a half" and "2½" all to the same thing.
telchar 9y ago
This seems relevant: https://open.blogs.nytimes.com/2015/04/09/extracting-structured-data-from-recipes-using-conditional-random-fields/ https://open.blogs.nytimes.com/2015/04/09/extracting-structu...

qwerty456127 9y ago

Cool! Does anybody also know a good blockchain crash course of similar kind so one could grok all the major buzzwords of today?

lanewinfield 9y ago
I loved this one a lot: https://anders.com/blockchain/ https://anders.com/blockchain/
- abhishekjha 9y ago
  This is what got me a proper intuition for why a blockchain can be useful.

redditmigrant 9y ago

As someone who is trying to learn ML, all the courses available are hugely helpful. One thing I wish I had easy access to is the process that someone goes through while trying to build a model on a real dataset. Specifically following questions are the ones I struggle with: 1. How did you figure out what features would be useful? 2. How did you figure out what algorithm(s) are appropriate? 3. how and why did you massage the data in a specific way?

disgruntledphd2 9y ago
If you are willing to do the work, Frank Harrell's Regression Modeling Strategies is a pretty good introduction to a lot of this. It's written for a very different set of problems than typical ML, but it has lots of really good advice for practical problems in data analysis and prediction (which is another term for ML). Mostly people learn this stuff by experience. Find a dataset, choose a predictor, filter, clean and massage your data till you get better metrics/understanding (preferably both). Rinse, repeat on many different datasets and problems, and you'll know how to do this.
bkanber 9y ago
> How did you figure out what features would be useful? There are various feature engineering and feature extraction techniques. Filter methods, wrapper methods, and embedded methods. Principle component analysis, autoencoding, variance analysis, linear discriminant analysis, Gini index, genetic algorithms, etc -- the feature selection process will depend on the dataset, the problem domain, the analysis algorithm you ultimately use, etc. > How did you figure out what algorithm(s) are appropriate? Also depends on the problem domain. Discrete or continuous data? Categorical features, numeric features, features as bitmasks. Do you need a probabilistic outcome? Etc. Generally you start with the easiest algorithms in your toolbox to see how viable they are. For a classification task I'll almost always start with a naive Bayes classifier (if the data allows) and/or a random forest and see how they perform. If the problem domain is highly non-linear you might start with a support vector or kernel method. Neural network is a last resort for me, as I find most classification problems can be solved to a high accuracy much more simply. > how and why did you massage the data in a specific way? This relates back to #1 -- you should only massage data based on what your feature engineering tells you to do. Sometimes you might want to remove outliers or clean up the training data, but only if the outliers really should be removed from consideration entirely.

suyash 9y ago

Is there something like this for Java programmers?

rripken 9y ago
I think Weka gets used in the java world. https://www.cs.waikato.ac.nz/ml/weka/ https://www.cs.waikato.ac.nz/ml/weka/
lovelearning 9y ago
ML concepts are independent of programming language. I suggest not to let language preferences stand in the way of listening to lectures and understanding the subject. For implementing exercises using Java, you have a bunch of good options: 1) The most direct equivalent to Pandas+Tensorflow I can think of is DL4J. They have a good comprehensive set of concept and implementation tutorials [1]. 2) TF APIs have a Java port and can be used from java desktop and console applications [2]. So a second but slightly more difficult option is using TF Java port + Spark APIs. [1]: https://deeplearning4j.org/documentation https://deeplearning4j.org/documentation [2]: https://www.tensorflow.org/install/install_java https://www.tensorflow.org/install/install_java

bjourne 9y ago

I want to ask people who know ML well if the hype is warranted? Billions of courses, web sites, job applications and HN posts. The subject seem to have taken off massively in the last two years. I mean image and speech recognition is pretty cool (when it works!), but hardly that earth shattering, is it?

bkanber 9y ago
The hype is and isn't warranted. ML is a much broader field than just neural networks. The hype for ML, in general, I think is warranted. We hit an inflection point when AWS launched and scalable processing power became cheap. It became cheap to process tons of data and generate insights. I don't have hard numbers on this, but probably 90-95% of machine learning used in practice is NOT neural networks, and have accuracies in the 90%+ arena. So ML in general -- sure, hype warranted. Neural networks are the new hot topic, and the hype isn't fully warranted yet. TensorFlow made them very popular in the developer community; this is a good thing because it's spurring more investment and research in ANNs. But for any given problem, odds are that a neural network is not the best (ie, most accurate or cheapest) way to solve it. Neural networks do have specific problem domains where they are the state of the art, but for most other problem domains there exists a better solution. So I'd say that neural networks are a little over-hyped right now, but with a new generation of developers learning about and experimenting with ANNs, that will change in a few years. I think we're about to see an explosion of ANN usefulness over the next few years. TLDR: ML is very useful but is more than neural networks; neural networks need a little more progress to catch up to the tensorflow hype.
ageitgey 9y ago
Computers are automation tools that increase human efficiency by doing the grunt work for you - but they are limited to automating the tasks that can be captured as a set of rules in code. When we figure a new way to model more complex tasks in code, a whole new set of things can be automated. Here's a concrete example: Before spreadsheets existed, there used to be legions of accountants who created complex ledgers on paper and added up all numbers to track how a business was doing. You'd literally mail off your sales numbers to an accounting team somewhere and wait three days to get the latest report generated and sent back. Sure they had calculators to add numbers, but the computers of the day didn't understand how those numbers related to each other. The human still had to do most of the work to create the reports. The big idea of spreadsheets was to make the computer manage the more complex task of knowing how different numbers in a report related to each other. It made most ledger tasks totally automatic once the initial report was defined. Now a single accountant could do the work of the entire accounting team - and more accurately and in less time! There were stories of the first spreadsheet testers having to delay mailing back their financial reports by a few days because their clients would be suspicious if they mailed them back too fast. Nearly overnight accounting got a lot more efficient and companies made more money. T"What If" modeling that used to be too slow and cost prohibitive to do was now it was quick and easy. Companies could plan more intelligently. The spreadsheet was a true game changer. This same pattern happens every time the bar is raised on the complexity of what can be automated and Machine Learning raises the bar one giant notch. Previously we were limited to automating tasks that a smart coder could describe as discrete steps in code. But with ML, the computer can figure out it's own rules just by looking at data. That means in many cases you can solve very hard problems just by collecting a lot of data. Lots and lots of things that used to be done by large groups of people will now be able to be done with a single computer. In that sense, ML is a total game changer. Don't focus on the specific applications thus far. Focus on the idea that all kinds of tasks that used to require humans can now be automated with a little bit of applied ML. The opportunities are literally everywhere. In a few years, ML won't be some esoteric technique used by a few people. It will be a core skill that everyone uses or touches in some way. It's going to creep into everything everywhere because it's just so darn useful.

rtfs 9y ago

Thanks Google! Now I know that I am a ML guy, as an economist and econometrician. Yes, we shoot this on all kind of stuff, though with a clear business acumen or economic policy thinking.

andyjohnson0 9y ago

This looks like a well put-together course, and a good way to learn TensorFlow. Keras and TensorFlow are top of my list of technologies to explore in the very near future. Is anyone here doing Andrew Ng's Machine Learning course [1]? I'm about half-way through and really enjoying it. I'm particularly appreciating that the programming exercises are done in MatLab/Octave, so I feel that I'm really understanding the fundamentals without an API getting in the way, and developing some good intuition. Obviously frameworks are the way to go for production ML work, but I wonder whether ML people here think this bottom-up approach is advisable or could it be misleading when I move on to Keras/TensorFlow/whatever? [1] https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning Edit: brevity

bkanber 9y ago
I teach ML and am currently writing my 2nd book on it. I always advocate learning the fundamentals. Machine learning is math, and neural networks in particular rely on linear algebra and vector calculus. (You can build a NN without using linear algebra directly, likely it'll be slower and besides, the concept still relies on linear algebra). Frameworks abstract away a lot of the mathiness, which is a net good for society (ie, exposing lots of developers to neural networks), but I consider that a net-negative for the individual developer. When working on anything but trivial toy problems, you should make sure you understand your problem domain and implementation thoroughly. Is the activation function you've chosen ideal for your problem domain? If not, choose a better one. If no better one exists, you can invent it; but you'll also need to know how to design the backpropagation algorithm for that new activation function (which requires some vector calculus). Learning the math, as you have, helps you tune your algorithm based on actual knowledge rather than guesswork. I don't think it will be misleading when you move on to a framework. The frameworks are built on the same math. That said -- if all you're looking to do is play around, then you don't need the math as much.
- andyjohnson0 9y ago
  Thanks for taking the time to write such a comprehensive reply - much appreciated. "ML is maths" is something that I'm getting used to now. I do have some real uses in mind for what I'm learning' both in my job and some side projects, particularly image feature recognition, and I'm looking forward so seeing how it all works in out. Thanks again!

zitterbewegung 9y ago

I like this move from google. Sure it is targeted for you to use Tensorflow but more courseware and MOOCs help everyone. I love doing self study and Tensorflow's tutorials are top notch. Since I can also use Tensorflow on my own hardware and anywhere else I really love better docs and MOOCs in general. What I really want to do is understand enough Tensorflow to reproduce other people's experiments in their papers on github and I think this would be one of the best ways to do this. Of course, this may eat into a bunch of companies that have paid programs for ML but its Google's prerogative to make ML cheaper and easier to deploy and learn so I am all for that.

bpesquet 9y ago

The choice of TensorFlow is a bit disappointing for a beginner-focused course which looks really solid otherwise. Business seems to have gotten priority over pedagogy in that case. I see TensorFlow as the Angular of machine learning: first on the market, powerful but unwiedly. Like Angular, it will ultimately get superseded by tools with a nicer API (scikit-learn, Keras) or more versatility (PyTorch). Like Angular, it's probably not the best choice for a beginner to invest time into.

dragandj 9y ago
Add to that that TensorFlow was practically a latecomer, not the first to market.

ivan_ah 9y ago

I love the prework section: https://developers.google.com/machine-learning/crash-course/prereqs-and-prework https://developers.google.com/machine-learning/crash-course/... It's a very good mix of topic and skills that I think everyone should learn, even if not directly planning to do ML or DL. If y'all are looking for a compact (and inexpensive) textbook on linear algebra that comes with all prerequisites you can check out: https://gum.co/noBSLA https://gum.co/noBSLA (disclaimer: I wrotes it)

DiogoMCampos 9y ago

How does this compare to the CS229 lectures by Andrew Ng? (the recorded lectures, not the MOOC)

manav 9y ago
CS229 @ Stanford is very math/proof intensive. This might be somewhat similar to CS221 or the Coursera course.

loreteye 9y ago

My friend partner was out dating his friend at work and he needed an hacker to help spy her device. He got (Roccoshadow (at) gmx (.) com) to help spy her iPhone,facebook,email,whatsapp,calls,skype and others.They literally helped him and now he have her whatsapp Spyed without her noticing.

rohitpaulk 9y ago

I've compiled this into a Todoist template you can import - it's got links to each module + times. To preview: http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google-ml-crash-course http://todotemplates.com/posts/HRtYanEq8zMgRL5fz/google-ml-c... To import directly: https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd1aspxi4rjqbaz.cloudfront.net%2F24fc6edd6dff480670528e9f842cb494_Google%2520ML%2520crash%2520course.csv https://todoist.com/importFromTemplate?t_url=https%3A%2F%2Fd...

msaharia 9y ago

On a sidenote, can someone talk about what kind of tools are being used to integrate the subtitles and scrolling behavior over a youtube video in this course? Is there an OS implementation?

aabajian 9y ago

While I like the idea, in principle, that you don't need a CS education to use AI/ML, I doubt it. Here's a problem that cropped up today: Our instance ran out of hard drive space on a training set of ~400,000 images. The individual images were only 375 GB, but took up 1.5T when converted to Numpy matrices. Why? The arrays were converted to standard int arrays (32-bit x 3 channels) when they could've fit into short (8-bit x 3 channels). Each image was 4x as large as it needed to be. You can certainly use high-level ML tools (like Keras), but it takes a great deal of work to wrangle your data into a usable format, and even more knowledge to debug an ineffective network.

blueside 9y ago

we recommend that students meet the following prerequisites:Mastery of intro-level algebra. You should be comfortable with variables and coefficients, linear equations, graphs of functions, and histograms Any book suggestions to getting up to speed in this area?

ropable 9y ago
Khan Academy probably has this covered.
- toomuchtodo 9y ago
  A shame Google doesn’t just link to the Khan Academy course.
  - earth2mars 9y ago
    not true. they do have khan academy link where applicable. for example for the algebra ones. check below link https://developers.google.com/machine-learning/crash-course/prereqs-and-prework#prerequisites https://developers.google.com/machine-learning/crash-course/...
megaman22 9y ago
I always liked the Saxon books[1], since they involved so much spaced repetition if you did the problem sets that it beat the symbolic manipulation into your long-term memory. [1] http://amzn.to/2FH3bXL http://amzn.to/2FH3bXL
deleted 9y ago
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jpamata 9y ago
If you're looking for books, have a look at Schaum's Outline of Precalculus [0]. Khan Academy [1] is also good and there's this MOOC on coursera called Data Science Math Skills [2]. [0] https://www.amazon.com/Schaums-Outline-Precalculus-3rd-Problems/dp/0071795596 https://www.amazon.com/Schaums-Outline-Precalculus-3rd-Probl... [1] https://www.khanacademy.org/math https://www.khanacademy.org/math [2] https://www.coursera.org/learn/datasciencemathskills https://www.coursera.org/learn/datasciencemathskills

gcb0 9y ago

this site is so broken on firefox mobile they should be embarrassed.

ak_yo 9y ago

Cool tutorial, but I'm not entirely sure what makes this ML -- aside from neural nets, this is more or less the material you'd encounter in a basic applied statistics or regression analysis course, minus material on estimating uncertainty, modeling survival or time-series data, and causal inference. I suspect you'd benefit more from a 50 minute tutorial on those than neural nets.

earth2mars 9y ago

They do have pre-reqs training material with reference links. https://developers.google.com/machine-learning/crash-course/prereqs-and-prework#prerequisites https://developers.google.com/machine-learning/crash-course/...

noahha 9y ago

serving videos from youtube without alternatives doesn't make it accessible. proxying for identified videos esspecially videos from this crash course would be useful.

Mashimo 9y ago
>serving videos from youtube without alternatives doesn't make it accessible. Why is that?

deleted 9y ago

[deleted]

Animats 9y ago

Can't view. Requires a Google account.

Tepix 9y ago
Have you tried to create one?

deleted 9y ago

[deleted]

DeepWorker 9y ago

The ulterior motive behind this is to increase usage of Google Cloud (according to this answer on Quora: https://www.quora.com/Why-did-Google-release-their-machine-learning-crash-course/answer/Carlos-Matias-La-Borde?share=00711d71&srid=XvlZ https://www.quora.com/Why-did-Google-release-their-machine-l...)