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Ask HN: Full-on machine learning for 2020, what are the best resources?
I want to focus on Machine Learning for this 2020 but I see to many options; Deep Learning, AI, Statistical Theory, Computational Cognitive and more... but to focus just on ML, where should I start? I work mostly as a data analyst on pharma where the focus is batch process.
- lymitshn 7y agoAFAIK FastAI courses are well recommended for their Deep Learning stuff but they also have ML course[0] Another usual recommendation is Elements of Statistical Learning book. Another option is finding a MOOC that you enjoy and following it. [0]http://course18.fast.ai/ml http://course18.fast.ai/ml
- pjmorris 7y agoThere's a MOOC that uses 'Introduction to Statistical Learning' by the authors of 'Elements of Statistical Learning', here: https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about https://lagunita.stanford.edu/courses/HumanitiesSciences/Sta...
- orware 7y agoI'm not an expert, but I had heard lots of good things about Fast.ai's online course/content: https://course.fast.ai/ https://course.fast.ai/ I've started/stopped a few courses with Georgia Tech's OMSCS program as well which might have been useful, but I still feel like I'm missing some of the mathematical foundation to allow me to make more sense of those courses so Fast.ai's approach seems like it could be a better fit for someone like myself that's more interested in the practical aspects of using it (I just haven't made the effort to go through their content myself).
- lettergram 7y agoI’d suggest: https://fast.ai https://fast.ai - good intro on practical neural networks. I wrote a guide to ML based NLP. We identify if a sentence is a question, statement or command using neural networks: https://github.com/lettergram/sentence-classification https://github.com/lettergram/sentence-classification The truth is you don’t need to understand all the math right away with neural networks. Mostly it’s getting an understanding of why you use a given layer, bias, etc and when. Once you get some intuition then I’d learn the math. That’s at least how I instruct others. In any case, there are lots of guides for any flavor. I’d start with deep learning and focus on the “practical” then move to the “theoretical”.
- aliveupstairs 7y agohttps://www.fast.ai/ https://www.fast.ai/ https://fast.ai https://fast.ai is unsafe.
- codingslave 7y agoHonestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml code on github to learn from, its really the best way. When you encounter a concept you need to understand, google the concept and learn the background info. This will give you a highly applied and intuitive understanding of solving ml problems, but you will have large gaps. Which is fine, unless you are going in for job interviews. Also bear in mind that courses like fast.ai (as you see plastered on here), aggresively market themselves by answering questions all over the internet. Its a form of SEO. EDIT (Adding this here to explain my point better): My opinion is that the theory starts to make sense after you know how to use the models and have seen different models produce different results. Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless in the application of ML, and only useful in the active research of new machine learning methods and paradigms. Courses make the mistake of mixing in that useless information. The same thing is true of the million different optimizers for neural networks. Why different ones work better in different cases is something you would learn when trying to squeeze out performance on a neural network. Who here is intelligent enough to read a bunch about SGD and optimization theory (Adam etc), understand the implications, and then use different optimizers in different situations? No one. I'm much better off having a mediocre NN, googling, "How to improve my VGG image model accuracy", and then finding out that I should tweak learning rates. Then I google learning rate, read a bit, try it on my model. Rinse and repeat. Also, I will throw in my consiracy theory that most ML researchers and such push the theory/deep stats requirement as a form of gatekeeping. Modern deep learning results are extremely thin when it comes to theoretical backing.
- echelon 7y agoThis. Learn top down, not bottom up. Watch maybe one or two short videos on back propagation. You don't need to be muddled in the theory and the math - you can become productive right away. Once you start playing with pytorch and tensorflow models (train them yourself or do transfer learning), you'll start to develop an intuition for how the network graphs fit together. You'll also pick up tools like tensorboard. Also, do transfer learning. It's so awesome to train on a publicly-available high quality and large data set, train for a lot of epochs for good problem domain fit, then swap out your own smaller data set. It's magical. I have a feeling that ML in the future will be like engineering today. You can learn by doing and don't need a degree or formal background to be productive and eventually design your own networks. I have no formal training (save one undergrad course that was way outdated in "general AI"), and I've designed my own TTS and voice conversion networks. I have real time models that run on the CPU for both of these, and as far as I know they're more performant than anything else out there (on CPU). Eventually you might start reading papers. (You'll be productive long before you need to do this.) Most ML papers are open access, but review (broad survey) articles might need pirating. Thankfully there are websites that can help you get these. The papers aren't hard to read if you've spent some time playing with the networks they pertain to. Read the summary, abstract, and figures before diving into the paper. It may take a few reads and some googling. You do not need to be a data scientist. Anybody can do it. That said, a good GPU will help a lot. I'm using two 1080Ti in SLI and they're pretty decent.
- autokad 7y agoDoes anyone have any resources for people with more advanced ML experience?
- boltzmannbrain 7y ago1. Find a paper you like/admire 2. Implement their methods from scratch (i.e. numpy not pytorch) 3. Experiment a bit, tweaking the models/algs to gain intuition 4. Repeat 1-3
- throwlaplace 7y ago> Implement their methods from scratch (i.e. numpy not pytorch) lol this is basically impossible and completely pointless. please show me a numpy implementation of BERT or CycleGAN or deformable convolutions (note that jax != numpy). it's like suggesting implementing a kernel to someone who wants to learn about virtual memory or scheduling. better advice would be take a paper and implement the model using pytorch without looking at their implementation and fiddle with that.
- wish5031 7y agoHave you read Goodfellow / Bengio / Courville’s deep learning book? The later chapters go in more depth than most other resources I’ve found.
- Tenoke 7y agoHonestly, I would start with fast.ai - if you dont like it by lesson 3 switch to another resource. If you do like it through fast.ai is probably the biggest bang for your buck(time).
- eachro 7y agoI think it depends on what you want to focus on. If you want to do deep learning, fast.ai is probably the best resource available. Jeremy Howard and Rachel Thomas (the two founders) have poured quite a lot into fostering a positive, supportive community around fast.ai which really does add quite a lot of value. If you want to really understand the fundamentals of machine learning (deep learning is just one subset of ML!), there is no substitute for picking up one of the classic texts like: Elements of Statistical Learning (https://web.stanford.edu/~hastie/ElemStatLearn/ https://web.stanford.edu/~hastie/ElemStatLearn/), Machine Learning: A Probabalistic Approach (https://www.cs.ubc.ca/~murphyk/MLbook/ https://www.cs.ubc.ca/~murphyk/MLbook/) and going through it slowly. I'd recommend a two pronged approach: dig into fast.ai while reading a chapter a week (or at w/e pace matches your schedule) of w/e ML textbook you end up choosing. Despite all of the hype of deep learning, you really can do some pretty sweet things (ex: classify images/text) with neural nets within a day or two of getting started. Machine learning is a broad field, and you'll find that you will never know as much as you think you should, and that's okay. The most important thing is to stick to a schedule and be consistent with your learning. Good luck on this journey :)
- jamesxv7 7y agoExcellent recommendation. I really appreciate all the recommendations proposed. Happy New Year eachro.
- blululu 7y agoThe answer to this question depends on your level of computer & math proficiency. Some folks here have been debating about the relative merits of practice vs. theoretical foundations, but this dispute makes some assumptions about where you are starting from and where you are most comfortable. The fastest way to learn something is to fit it into a framework that you already understand. If you have a PhD in theoretical physics/abstract mathematics (like a lot of ML researchers), then the more mathematical (theoretical) frameworks will be a good way to build deep intuitions. If, on the other hand, you are more into applied data analysis, then you will probably find that working on applications will be the easiest way to go. Personally, I enjoyed both Andrew Ng's and Geoffrey Hinton's respective courses on ML and Neural Networks on Coursera. You may also want to check out Michael Neilsen's online essay on deep learning (http://neuralnetworksanddeeplearning.com http://neuralnetworksanddeeplearning.com). Ultimately I would also encourage you to supplement your understanding by applying this work to your own applications. The universe is often the best teacher.
- 1996 7y agoWhoever read this - please please please ignore the posts that suggest to just play with numbers. This is the equivalent of suggesting to someone who wants to learn how to code to copy-paste formulas into excel. Just don't be that person. To be very blunt, in 2020 most ML is still glorified statistics, except you lose the insights and explanations. The only tangible improvements can be random forests - some times. 99% of the stuff you can do with basic statistics. 99% of the coders I know just don't know statistics besides the mean (and even with that, they do senseless things like doing means of means) So learn statistics - basic statistics, like in the "for dummies" book series. If you want to be a little more practical, stats "for dummies" is often found in disciplines that depends on stats, but are not very good in math - biology, psychology, and economics are great candidates. So just download biology basis stats (to know how to compare means - this gives you the A/B test superpower), then psychology factor analysis (to know PCA - this gives you the dimension reduction superpower) then econometrics basic regression (to know linear regression) With these 3 superpowers, you will be able to do more than most of the "machine learning" people. When you have mastered that, try stuff like random forest, and see if you still think it's as cool as it's hyped to be.
- rckoepke 7y agoYou can do pose estimation with basic statistics?
- hcho3 7y agoMany business data is tabular (possibly with time component), and if you are working with tabular data, the OP’s advice is sound.
- hcho3 7y agoGiven that many data people run across is tabular, I appreciate your advice about the importance of statistics. Also kudos for mentioning hypothesis testing (no one in this thread mentioned it). Lastly, I’d add that ML practitioners will gain a lot by listening to statisticians and economists on the issue of data quality, e.g. selection bias. That said, I am not as cynical about “machine learning.” ML and “data science” brought the importance of prediction front and center, i.e. can you fit a model that accurately predict the target value given a previously seen input? This point is made by the recently published stats textbook Computer Age Statistical Inference (Efron and Hastie). In some applications, it may be beneficial to choose black box models with high predictive accuracy, as the goal for these applications is prediction, not interpreting individual model coefficients.
- thosakwe 7y agoDoes anybody have resources on the math behind ML? I hit a dead end using Python frameworks because it was a black box, and I simply lacked the underlying knowledge.
- olalonde 7y agoWeek 1-5 of https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
- SaxonRobber 7y ago100-page ML book for a brisk tour Deep Learning (Goodfellow) Introduction to Statistical Learning
- hakmad 7y agoMathematics for Machine Learning - https://mml-book.github.io/ https://mml-book.github.io/
- olalonde 7y agoI started with with the machine learning course[0] on Coursera followed by the deep learning specialization[1]. The former is a bit more theoretical while the latter is more applied. I would recommend both although you could jump straight to the deep learning specialization if you're mostly interested in neural networks. [0] https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning [1] https://www.coursera.org/specializations/deep-learning https://www.coursera.org/specializations/deep-learning
- jansc 7y agoHumblebundle has a bundle of machine learning books right now: https://www.humblebundle.com/books/python-machine-learning-packt-books https://www.humblebundle.com/books/python-machine-learning-p... I'm considering buying this bundle. Any of these books you would recommend?
- jansc 7y agoI'm not affiliated with humblebundle in any way, and this was a genuine question. I know that the packt books are not best quality, but if one these books is a good introduction to practical ML, I would consider it a good deal. In my opinion much better than googling algorithms and tutorials and visiting 10s or 100s of sites full of ads and ad trackers to find a suitable algorithm for a given problem. Reading an EPUB on my daily commute sounds much better and works offline.
- codingslave 7y agoPaid shill. See this duplicate comment on reddit: https://old.reddit.com/r/MachineLearning/comments/ei2iz6/d_is_this_bundle_worth_buying/ https://old.reddit.com/r/MachineLearning/comments/ei2iz6/d_i...
- vjktyu 7y agoStudy calculus, from the definition of real numbers and to taking complex integrals via residuals; then study linear algebra to some theorems about eigenvectors. 1 month total, assuming you're somewhat talented and determined to spend 12 hours a day learning proofs of boring theorems. After that you'll realise that most of the ML papers out there are just ad-hoc composed matrix multiplications with some formulas used as fillers. At that point I think it's more useful to learn what ML models work in practice (although nobody will be able to explain why they work, including the authors) and mix this practical knowledge with the math theory to develop good intuition. I'd compare ML with weather models: we understand physics driving individual particles, we understand the high level diff equations, but as complexity builds up, we have to resort to intuition to develop at least somewhat working weather models.
- Buttons840 7y agoAnother suggestion: I like https://spinningup.openai.com https://spinningup.openai.com for learning reinforcement learning.
- psv1 7y agoGood free resources: - MIT: Big Picture of Calculus - Harvard: Stats 110 - MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website. - Machine Learning: A Probabilistic Perspective - Murphy That's a lot of material to cover. And at some point you should start experimenting and building things yourself of course. If you'are already familiar with Python, the Data Science Handbook (Jake Vanderplas) is a good guide through the ecosystem of libraries that you would commonly use. Things I don't recommend - Fast.ai, Goodfellow's Deep Learning Book, Bishop's Pattern Recognition and ML book, Andrew Ng's ML course, Coursera, Udacity, Udemy, Kaggle.
- vowelless 7y agoWhy don't you recommend fast.ai and kaggle?
- mathisonturing 7y agoand Andrew Ng's ML course?
- joshvm 7y agoBear in mind Elements of Statistical Learning is a grad-level text. I would never recommend that to a beginner to the field over an Introduction to Statistical Inference, by the same authors. Geron Aurelien's Oreilly book is great - Hands-On Machine Learning with Scikit-Learn and TensorFlow. Get the second edition which covers Tensorflow 2.
- psv1 7y agoYou're right about ESL, that's why I started the list with some more fundamental material. Also, +1 for Aurelien's book, it's really good; I didn't know he had a revised edition for TensorFlow 2.
- vowelless 7y agoA lot of good advice here. One thing I would add is replicate a couple of ML papers. It can help develop a lot of intuition about the specific area.
- jamesxv7 7y agoActually this is a great idea. Seems I'll try this approach for 2020 Q1.
- jamesxv7 7y agoI'm impressed by the responses generated in this conversation. My expectation was to get several links and start browsing each one of them. However, many have agreed that the best way is to start with a specific example and start creating a model. Many times I have tried to answer that same question, "which model to apply"? How do I know I'm not re-inventing the wheel?
- mkl 7y ago> How do I know I'm not re-inventing the wheel? You probably are, but for learning purposes that doesn't matter at all.
- pretendscholar 7y agoIf you successfully invent a new, improved, wheel then you don't need help or guides.
- e_ameisen 7y agoA lot of the resources proposed in the comments focus on theoretical knowledge, or a particular sub-domain (Reinforcement Learning, or Deep Learning). I recommend a top down approach where you pick a project and learn by building it. This can be easier said than done however, and after mentoring dozens of junior Data Scientists I wrote a how-to guide for people interested in using ML for practical topics. You can find it from O'Reilly here (http://shop.oreilly.com/product/0636920215912.do http://shop.oreilly.com/product/0636920215912.do) or on Amazon here (https://www.amazon.com/Building-Machine-Learning-Powered-Applications/dp/149204511X/ https://www.amazon.com/Building-Machine-Learning-Powered-App...).
- DrNuke 7y ago> where should I start? I work mostly as a data analyst on pharma where the focus is batch process. Any tool needs an applied field but any applied field does not need all the tools. You have an applied field already (pharma), so start looking for one or two state-of-the-art ML papers for that? Happy 2020 and good luck, it’s going to be fun!
- bigmit37 7y agoIs C/C++ still worth learning if o want to create some models from scratch (new layers or different paradigms) I hear that C++ is a nightmare to work with and was wondering if Rust,Julia, or even Swift would be worth learning instead. I know Python but deep learning frameworks seem to be written in C++, so to come up with new layers I need to understand C++, which I was told has lot of peculiarities that takes time to pick up. Compiler isn’t also very user friendly (what I’ve read)
- LeftHandPath 7y agoC++ is not as tricky as people make it out to be. There is a lot of elitism among programmers, and a lot of people seem to claim it’s hard solely to make themselves look smarter for being able to write it. If you know the basics of programming and have the persistence to. RTFM (Read The Fucking Manual), C++ will not give you any trouble. In fact, you might actually start to enjoy it more than the other languages you used in the past. All that said, if you are focusing on machine learning rather than programming, then you should look into Python and R. A great resource is “an introduction to data science with R” by David Langer: https://m.youtube.com/watch?v=32o0DnuRjfg https://m.youtube.com/watch?v=32o0DnuRjfg
- bigmit37 7y agoIf this is the case I would actually love to play around with C++ as a lot of software that Python wraps around is written is in it and it gives me chance to look a little deeper into the source code.
- tehsauce 7y agoYou can actually implement most new layers or experimental ideas using frameworks like pytorch or tensorflow. They support fairly low-level primitives which are much more flexible than keras or pytorch sequential models. That said C/C++ is still very useful for implementing high performance systems.
- bigmit37 7y agoAh. I haven’t played around with Pytorch custom layers enough so I am going to give it a try. I was initially trying to do it in keras but Keras was just using tensorflow layers for most operations so I couldn’t tweak the original tensorflow layers through keras easily.
- zyl1n 7y agoBe sure to check out 3Blue1Brown's linear algebra series as well. (Maybe after you've built your own MNIST network) Blew my mind when I made the connection that each layer in a dense NN is learning how to do a linear transformation + a non-linear "activation" function.
- jamesxv7 7y agoThere is a question I have been asking for quite some time. It is known that Python is the language of choice when practicing ML. But, can similar results be achieved using Powershell? What makes Python superior to Powershell when making models for ML?
- mlboss 7y agoLibraries
- rckoepke 7y agoTechnically you can do it in any language, but in software engineering we tend to stand on the shoulders of giants in order to get the job done on time. A lot of original excellent data processing, statistical analysis, and ML libraries were built into Python and R, so all the deep learning stuff was built on top of those. R is somewhat harder to integrate into a production pipeline due to its typical reliance on something like RStudio, so Python ended up being the de facto standard as it is also well supported in cloud computing environments. With TensorFlow API's being written for Swift, we might start to see Swift competing with Python.
- lordgrenville 7y agoWow, I would never think to use Powershell outside of some Windows-specific tinkering. I guess every language has its diehard fans.
- pinouchon 7y agoNo one suggested standford cs231n: http://cs231n.github.io/ http://cs231n.github.io/. I'd recommend the winter 2016 lectures (by FeiFei Li, Karpathy and Johnson). For getting started with convnets / deeplearning, I think this is one of the best hands on ressources out there.
- asfarley 7y agoI'm using machine learning to solve some computer-vision problems. If you're interested in joining my project, email me at alex at roadometry.com
- StClaire 7y agoI was in the same boat in 2014. I went a more traditional route by getting a degree in statistics and doing as much machine learning as my professors could stand (they went from groaning about machine learning to downright giddy over those two years). I worked as a data scientist for an oil-and-gas firm, and now work as a machine learning engineer (same thing, basically) for a defense contractor. I’ve seen some really bad machine learning work in my short career. Don’t listen to the people saying “ignore the theory,” because the worst machine learning people say that and they know enough deep learning to build a model but can’t get good results. I’m also unimpressed with Fast AI for the reasons some other people mentioned, they just wrapped PyTorch. But also don’t read a theory book cover-to-cover before you write some code, that won’t help either. You won’t remember the bias-variance trade-off or Gini impurity or batch-norm or skip connections by the time you go to use them. Learn the software and the theory in tandem. I like to read about a new technique, get as much understanding as I think I can from reading, then try it out. If I would do it all-over again I would: 1. Get a solid foundation in linear algebra. A lot of machine learning can be formulated in terms of a series of matrix operations, and sometimes it makes more sense to. I thought Coding the Matrix was pretty good, especially the first few chapters. 2. Read up on some basic optimization. Most of the time it makes the most sense to formulate the algorithm in terms of optimization. Usually, you want to minimize some loss function and thats simple, but regularization terms make things tricky. It’s also helpful to learn why you would regularize. 3. Learn a little bit of probability. The further you go the more helpful it will be when you want to run simulations or something like that. Jaynes has a good book but I wouldn’t say it’s elementary. 4. Learn statistical distributions: Gaussian, Poisson, Exponential, and beta are the big ones that I see a lot. You don’t have to memorize the formulas (I also look them up) but know when to use them. While you’re learning this, play with linear regression and it’s variants: polynomial, lasso, logistic, etc. For tabular data, I always reach for the appropriate regression before I do anything more complicated. It’s straightforward, fast, you get to see what’s happening with the data (like what transformations you should perform or where you’re missing data), and it’s interpretable. It’s nice having some preliminary results to show and discuss while everyone else is struggling to get not-awful results from their neural networks. Then you can really get into the meat with machine learning. I’d start with tree-based models first. They’re more straightforward and forgiving than neural networks. You can explore how the complexity of your models effects the predictions and start to get a feel for hyper-parameter optimization. Start with basic trees and then get into random forests in scikit-learn. Then explore gradient boosted trees with XGBoost. And you can get some really good results with trees. In my group, we rarely see neural networks outperform models built in XGBoost on tabular data. Most blog posts suck. Most papers are useless. I recommend Geron’s Hands-On Machine Learning. Then I’d explore the wide world of neural networks. Start with Keras, which really emphasizes the model building in a friendly way, and then get going with PyTorch as you get comfortable debugging Keras. Attack some object classification problems with-and-without pretrained backends, then get into detection and NLP. Play with weight regularization, batch norm and group norm, different learning rates, etc. If you really want to get deep into things, learn some CUDA programming too. I really like Chollet’s Deep Learning with Python. After that, do what you want to do. Time series, graphical models, reinforcement learning— the field’s exploded beyond simple image classification. Good luck!
- SrslyJosh 7y agoAsk yourself: Do you really need ML to solve the problems you're interested in solving? If you're learning it for career purposes, keep in mind that many corporate ML use-cases are problematic at best. At worst, you will produce something that kills someone inadvertently, possibly more than one person. Learn about the many pitfalls and limitations of ML. Learn about inadvertent bias in datasets. Learn about the issues with inputs not represented (or not adequately represented) in your training dataset. Most importantly, understand that ML is not magic and without significant guardrails in place, there's a good chance something will fuck up.
- sytelus 7y agoIn following order: 1. Michael Nielson's book: http://neuralnetworksanddeeplearning.com/ http://neuralnetworksanddeeplearning.com/ 2. Stanford CS231n course: http://cs231n.stanford.edu/ http://cs231n.stanford.edu/ 3. DRL hands on book: https://www.amazon.com/Deep-Reinforcement-Learning-Hands-Q-networks/dp/1788834240 https://www.amazon.com/Deep-Reinforcement-Learning-Hands-Q-n... After this churn through research papers or medium articles on conv net architecture surveys, batchnorm, LSTM, RNN, transformers, bert. Write lots of code, try things out.
- fantispug 7y agoThis may make sense if you want to do image processing and deep reinforcement learning. But there are lots of other domains. For tabular data (which is probably most relevant in Pharma, and probably the best place to start) Introduction to Statistical Learning by Hastie et al and Max Kuhn's Applied Predictive modelling cover a lot of the classical techniques. For univariate time series forecasting "Forecasting Principles and Practice" is great. For natural language processing foundations Jurafsky's Speech and Language Processing is broadly recommended; for cutting edge natural language processing Stanford's CS224n is great: http://web.stanford.edu/class/cs224n/ http://web.stanford.edu/class/cs224n/
- Breza 7y agoI can't suggest Introduction to Statistical Learning enough, it's a fantastic book! I loaned my copy to another data scientist because I didn't want to hog such a valuable resource.
- joaogui1 7y agoMachine Learning: * https://www.youtube.com/watch?v=UzxYlbK2c7E https://www.youtube.com/watch?v=UzxYlbK2c7E: Andrew Ng's machine Learning course, the recommended entry point by most people * https://mlcourse.ai/ https://mlcourse.ai/ : More kaggle focused, but also more modern and has interesting projects Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects Deep Learning * https://www.fast.ai/ https://www.fast.ai/ - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders" * https://www.coursera.org/specializations/deep-learning https://www.coursera.org/specializations/deep-learning : More bottom-up approach, helps to understand the theory better Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects. After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts (https://azeria-labs.com/the-importance-of-deep-work-the-30-hour-method-for-learning-a-new-skill/ https://azeria-labs.com/the-importance-of-deep-work-the-30-h...)
- forgingahead 7y agoI had a nice experience with Adam Geitgey's Machine Learning is Fun course. He published a lot of free ML blog posts, in easy-to-understand writing with nice examples, so it never made anything seem out-of-reach. I found that a lot of other material was a little too abstract, so his stuff was great. The blog posts are here: https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471 https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec... And I also bought his paid course with code samples -- it's affordable and good value.
- atregir 7y agoExcellent thread - I have the same goal and currently am working mostly with databases. Thanks for asking this question!
- 6ak74rfy 7y agoI am currently going through fast.ai's Deep Learning course and will totally recommend it because of its top-down approach. Has anyone done non-DL courses on their website? For e.g., any thoughts on Rachel's Computational Linear Algebra?