A five-course Coursera specialization by Andrew Ng, offered through deeplearning.ai, covering neural networks, hyperparameter tuning and project structure, convolutional networks, and sequence models. It builds from the math of neural networks up to applied deep learning.

Commenters call it excellent and phenomenal, praise its theory-first approach that builds understanding of what happens under the hood, and say the assignments help clarify concepts.

What commenters said (38)

1. Strongly recommendslevocardia2025in “Coursera to combine with Udemy”

   Ironically the very first coursera course was (IIRC) Andrew Ng's machine learning course, which was fantastic, and the deep learning specialization, which was also phenomenal. I can unironically say that Andrew Ng was the best instructor I ever had in grad school (and I didn't go to Stanford...).

2. Strongly recommendsmanimino2023in “Ask HN: ML Papers to Implement”

   I enjoyed doing this through the Coursera deep learning specialization:

   https://www.coursera.org/specializations/deep-learning

   The lectures take you through each major paper, then you implement the paper in the homework. Much faster than reading the paper yourself.

3. Strongly recommendsstriker_axel2020in “Ask HN: Which Coursera courses/specializations you recommend?”

   I would recommend Andrew Ng's Deep Learning Course. https://www.coursera.org/specializations/deep-learning

   This course is extremely good mostly because it covers the essential theoretical topics and gives some practical advice. TIP: do solve the assignments bcz it will clear a lot of concepts while solving it. ( or other solution can be found on github )

4. Strongly recommendsmark_l_watson2018in “Machine Learning Crash Course”

   Andrew Ng’s older machine learning MOOC class was excellent. I took it once, and took it again a few years later. In the last 8 months I also took his new deep learning set of courses. All really good stuf! (I have been working in the field since the 1980s, but constantly refreshing helps me. And Andrew’s lectures are great fun, he is a teaching artist.

5. Strongly recommendsmarrowgari2018in “Matrix Calculus for Deep Learning”

   If you're looking at this with the intention of getting started in Deep Learning and feeling overwhelmed by the math then Andrew Ng offers a great course on Coursera that goes over all of the formulas needed to calculate the forward propagation, loss computation, backward propagation, and gradient descent. Highly recommend it for anyone interested in breaking into the field of machine learning.

6. Strongly recommendspongolyn2018in “Ask HN: How to Seriously Start with Machine Learning and AI”

   I started with Andrew Ng's ML courses, which take a bottom-up approach beginning with the math. After finishing the deeplearning.ai track, I started rounding out my skillset with some data science and R programming classes, so I could be more comfortable working with unfamiliar data.

   The fast.ai courses are a more top-down approach to ML, and there are plenty of good reasons for taking this approach. You'll start getting practice with libraries like Tensorflow right away. However, if you have a fairly strong math background and linear algebra doesn't give you nightmares, I highly recommend the Andrew Ng courses. A deep (ha!) understanding what's going on "under the hood" in ML will help your debugging, inform your strategy, and make your code better in the long run.

7. Strongly recommendstosser3502017in “Why Must You Pay Sales People Commissions?”

   I think MOOCs are the future based on my own experience. They allow the best teachers to scale their reach. Instead of aristotle giving a lecture to a room it allows an unlimited audience who can also rewind and pause if they don't understand something.

   I've been able to learn machine learning and deep learning from Andrew Ng, one of the best AI researchers in the world, among other world class researchers, for free. It's incredible when you think about it, probably one of the most significant things in human history. I've been able to learn how to code by picking and choosing the best classes from MIT, Stanford, Harvard, etc without spending a penny. Why settle for mediocre teachers at state universities when these options are available?

   The major thing we need to do is find some other way to grade people other than using a college degree as a benchmark.

8. Recommendsgradascent2024in “Llama3 implemented from scratch”

   If you want to gain familiarity with the kind of terminology you mentioned here, but don't have a background in graduate-level mathematics (or even undergrad really), I highly recommend Andrew Ng's "Deep Learning Specialization" course on Coursera. It was made a few years ago but all of the fundamental concepts are still relevant today.

9. Recommendsalessiodm2024in “Deep Reinforcement Learning: Zero to Hero”

   I took the Deep Learning course [1] by deeplearning.ai in the past, and their resources where incredibly good IMHO. Hence, I would suggest to take a look at their NLP specialization [2].

   +1000 to "Neural networks: zero to hero" already mentioned as well.

   [1] https://www.deeplearning.ai/courses/deep-learning-specializa... [2] https://www.deeplearning.ai/courses/natural-language-process...

10. RecommendsReebz2024in “Ask HN: AI Training for Executive Level”

    Executive level education is a tricky tightrope to walk in my opinion - often you’ll hear upon completion “I now know enough to be dangerous!”. Practically, and I mean this in the most endearing way, it translates to “I know enough to be confidently incorrect”.

    AI is a wide field. The hot topic is generative AI.

    If you really want to learn the depths, I’d start with brushing up on statistics 101 and 201. At least you’ll know how to run controlled experiments to see if these AI work.

    For practical, hands-on learning it’s hard to go past either Jeremy Howard’s FastAi courses or Andrew Ng’s deep learning courses. The latter is via Coursera from memory, so you can put a little sticker on your LinkedIn if that’s a factor.

    For executive education, I’d point back to my opening quote. Either do the hands-on learning that is rapid or sign up to a 2-year exec Masters program. MIT offer a fantastic, full-course load Exec MBA (that you get a regular MBA diploma at the end, it’s not watered down in accreditation or effort). In doing so, you can specialize heavily in AI/ML and you will be coding in those classes.

11. Recommends__rito__2023in “Generative AI learning path”

    Learn Machine Learning first. Andrew Ng's course is great. Then learn Deep Learning through Andrew NG's course on Coursera.

    Then go through fast.ai for practical projects.

    Learn PyTorch well. There are many books around. I like the one from Manning and the one by Sebastian Raschka.

    Then chart your own path from there.

    Many people "learned Math" in college, but these were actually mostly mindless following of algorithms to solve problems by hand. If you want proper refreshers, go through ICL's Mathematics for Machine Learning Specialization on Coursera or 3blue1brown's series on Linear Algebra and Calculus.

12. Recommendswaynerad2023in “Looking for recommendations on AI/ML online courses”

    I highly recommend the Deeplearning.AI courses by Andrew Ng.

13. Recommendsrg1112023in “Ask HN: What are the foundational texts for learning about AI/ML/NN?”

    Do you have Linear Algebra knowledge, and Stats 101 knowledge?

    Then start with ISLR.

    Then go and watch Andrew Ng Machine Learning course on Coursera (a new version was added in 2022 that uses Python).

    Then read the sklearn book from its maintainers/core devs. It's from O'Reilly.

    Then go do the Deep Learning Specialization from deeplearning.ai.

    Then do fast.ai course.

    If interested in Deep RL, watch David Silver lectures, then read Deep RL in Action by Zai, Brown. Then do the HF course on Deep RL.

    This is how you get started. Choose your books based on your personality, needs, and contents covered.

    And among MOOCs, I highly suggest the one by Canziani, LeCun from NYU. (I loved the 2020 version.)

    The one taught by Fei Fei Li and Andrej Karpathy is nice.

    These two MOOCs can substitute classic books based on quality.

    I have never read cover to cover any of the famous books. I read a lot from them sticking to specific subjects.

    Get to reading papers, finding implementations. Ng + ISLR will give you good grounds. Fast.ai + deeplearning.ai will give you capability to solve real problems. NYU + Tubingen + Stanford + UMich (Justin Johnson) courses will bring you to the edge.

    You need a lot of practical experience that aren’t taught anywhere. So, get your hands dirty early. Learn to use frameworks, cloud platforms, etc.

    Then start reading papers.

    A crystal clear grasp on Math foundations is a must. Get it if you don't have already.

14. RecommendsSalgat2022in “John Carmack Leaves Meta”

    Andrew Ng's online courses are great to get your feet wet. You use Octave/Matlab to implement the basics of many machine learning models from scratch, and build yourself up to using python to design several popular deep learning models including convolutional and transformers. It's not required, but a good idea to understand at least the basics of linear algebra and calculus.

15. Recommendselephanlemon2022in “From Deep Learning Foundations to Stable Diffusion”

    It’s a bit dated now, but the DeepLearning.ai GANs specialization covers topics through StyleGAN. If you have no experience with ML at all I would probably start with the first two courses of their Deep Learning specialization and then jump into the GANs specialization.

    I would also highly recommend FastAI’s Deep Learning for Coders (and their new course that came out this year). You’ll start immediately with some cool applications (basic image recognition and NLP) and then drill down from there to learn how they work in detail.

    It’s set up such that you can learn as much as you want (basics with no depth: first chapter; basic understanding of how a neural network is trained with SGD: first four chapters; understanding of decision trees, LSTMs, and CNNs: first half; detailed understanding of how to build everything from scratch: whole book).

16. Recommendsrg1112022in “Practical Deep Learning for Coders 2022”

    Definitely do the fast.ai course. Totally worth it.

    But also use ISLR, Goodfellow, Bishop, etc.

    Start with Andrew Ng's ML, then do the first part of Aurelien Geron book, then do Ng's DL specialization, then do fast.ai. Then learn PyTorch. A great book would be Sebastian Raschka's book. Also d2l.ai. A fast-paced, but really good course would be the Neuromatch DL tutorials.

    Then move forward based on your interests.

    Yann LeCun has THE best MOOC on DL on YouTube.

    For the Math, I majored in Physics, so stuff came naturally. I suggest Imperial London's MOOC on Mathematics for ML specialization, Robert Ghrist's Calculus course, VMLS book for Linear Algebra. For stats, haven’t found a good one yet.

    What you read, how much- these all depend on what you want to do. Where do you want to see yourself, and so on.

    If you just want to brag about DL and put it on your resume so that you can get a job writing SQL queries and make PowerBI presentation as a "Data Scientist", then the bars are low.

    If you want to do some DL, then that is another league altogether.

    You need to be able to quickly read papers, understand ideas, use those for your own projects or papers.

    Makes sense?

17. Recommendsbrutus12132022in “Ask HN: Looking for Junior Developer Vacancy for a University Level Maths Tutor”

    Being a web developer is a terrible waste of talent if your friend is truly good at University level maths. Can I suggest he consider data science or deep learning? Check out coursera/deeplearning.ai's moocs; he can get a feel for it by going to youtube and checking out similar material.

18. Recommendssriram_malhar2022in “Machine Learning with PyTorch and Scikit-Learn”

    I suggest starting with Andrew Ng's Deep Learning Specialization set of courses. It is a very decent overview.

19. Recommendslobo_tuerto2021in “Ask HN: What are the best data science bootcamps?”

    Have a look at the courses offered by DeepLearning:

    https://www.deeplearning.ai/

    https://www.coursera.org/specializations/deep-learning

20. Recommendsystad2020in “Ask HN: Switching career path at 36. What is the right way”

    It is never too old to transition and 36 is not old. Have a growth mindset. Don't listen to anyone who says it is too old to switch. You have one life to live, do what makes you happy.

    Here is one advice, check that you want to move to AI/ML for the right reasons, not because it is the next cool thing.

    One strategy: Take some of the courses in ML, for example Andrew Ng's course on ML and Deep learning. If you enjoy the course, next find a real project to do and see if you truly enjoy it. If you do enjoy it then switch, otherwise Java programming is always there. I have found real ML is a lot different from Andrew Ng's course for example, pretty much like doing algorithms in class is different from real world programming.

21. Recommendssilverdrake112020in “Ask HN: Recommend me a course on Coursera”

    I would recommend Andrew Ng's updated course on Deep Learning with python instead. https://www.coursera.org/specializations/deep-learning

22. Recommendsolalonde2019in “Ask HN: Full-on machine learning for 2020, what are the best resources?”

    I 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

    [1] https://www.coursera.org/specializations/deep-learning

23. Recommendspmulv2018in “Curated Resources to get your ML journey started”

    Here are some others that are also commonly recommended on HN:

    Andrew Ng's Machine Learning MOOC: https://www.coursera.org/learn/machine-learning

    Andrew Ng's Deep Learning Courses: https://www.deeplearning.ai/

    David Silver's Reinforcement Learning Youtube Series: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

    Fast.ai Courses on ML and Deep Learning: https://www.fast.ai/

24. RecommendsJagat2018in “Microsoft makes AI training courses available to the public”

    You should consider taking this course series "Mathematics for Machine Learning" from coursera. https://www.coursera.org/specializations/mathematics-machine...

    However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math. It'll help you see where the math you're learning is actually used, as you learn them.

    Try deeplearning.ai first, then try this "math for ML" course.

25. Recommendsmlboss2018in “Ask HN: What are the best MOOCs you've taken?”

    Why not both ? Both are excellent courses. I took deeplearning.ai last year. This year I am planning to complete fast.ai.

26. Recommendsmewwts2018in “Ask HN: How to getting started in AI/ML?”

    Hey!

    I'd suggest starting with this excellent course:

    https://www.coursera.org/learn/machine-learning

    And then dive into competitions on kaggle.com.

    Then following up with the deeplearning.ai specialization here: https://www.coursera.org/specializations/deep-learning

    and the http://www.fast.ai/ courses.

    You'll be up to speed in no time :)

27. Recommendssupermdguy2017in “Turi Create simplifies the development of custom machine learning models”

    Ng's deeplearning.ai is good for getting a solid background in how it all works. If you want to just jump into building things, I'd highly recommend http://course.fast.ai.

28. Recommendsinit-as2017in “Turi Create simplifies the development of custom machine learning models”

    I you're looking to learn about the maths behind neural nets and really understand how they work I'd recommend Andrew Ng's deeplearning.ai course. Starts from scratch and will give you a deep understanding when you're done.

29. Recommendsjackthetab2017in “A Neural Network in 11 Lines of Python (2015)”

    You may want to check out Andrew Ng's Deep Learning Specialization over on Coursera. [1] One of the courses is specifically about hyperparameter tuning and another about structuring your project. There is a lot of practical information scattered across all the courses.

    Yes, I'm taking the specialization and having a blast with it. :-)

    [1] https://www.coursera.org/learn/neural-networks-deep-learning

30. Recommendsdagw2017in “TensorFlow Tutorial and Examples for beginners”

    Agreed about scikit-learn. Anecdotally I've found that most "deep learning" and "big data" problems I see in industry could be trivially solved using scikit-learn on a reasonably powerful laptop.

    Also if you want to really understand neural networks I can recommend Andrew Ng's new deep learning course on Coursea. You start with deriving the underlying math and implementing a simple (but fully functional) neural network using just basic numpy, so that when you get to tensor flow and the latest cutting edge techniques you actually understand what's happening under the hood and how things actually work.

31. Recommendswadams192017in “Ask HN: What maths are critical to pursuing ML/AI?”

    Totally depends on where you want to land on the engineering-AI-products to pure-AI-research spectrum.

    So, what do you mean by "pursuing"?

    But even still, I would caution against trying to upload a bunch of new math concepts into your brain without first understanding the ML/AI context.

    I would say go through both of Andrew Ng's ML and DL courses on Coursera.

    Then, pick a domain/ problem that you're interested in.

    Then, read papers about how ML/AI is applied in that domain.

    Then, try to reproduce a paper that you understand and are interested in.

32. Recommendsyodaarjun2017in “Ask HN: What free resources did you use to learn how to program ML/AI?”

    For Deep Learning, deeplearning.ai has launched a free course on Coursera, which you may want to check out.

33. MentionsSlix2023in “Dive into Deep Learning: Interactive deep learning book with codes and maths”

    I'm running through Andrew Ng's "Machine Learning Specialization" course on Coursera right now. I plan to do his "Deep Learning Specialization" immediately after.

    There's not a lot of formulas and math so far, which concerns me because I eventually want to understand papers.

34. Mentionsilaksh2020in “How to Learn Machine Learning and Deep Learning: A Guide for Software Engineers”

    Ng's Deep Learning Specialization is awesome but I feel after this second course I need to stop and create my own exercises to build and apply NNs from scratch because I am learning how to convert math notation to vectorized Python code as much as anything else. I mean I am learning some core concepts along the way which I definitely appreciate and the reason I am continuing, but to become proficient I need to apply them without the hand-holding. Because those Coursera assignments have evolved to be computer-graded and easily approachable and so they actually do most of the thinking for you.

    Also I feel the modern approach is not vectorized Python but something like Keras or Tensorflow 2 or anything that is maybe higher level or will allow me to keep the computation on the GPU/TPU and not going back and forth to the CPU like I assume numpy must be doing.

35. MentionsTinyBig2020in “Coursera valued at $2.5B after a finance round of additional $130M”

    I've taken a few courses on Coursera, including the deep learning specialization, and I thought the material was great. However, I noticed that in courses with ~5 hours of video, quizzes, and a project, the average completion times were measured in minutes. Based on this, I assume most people who received certificates cheated and didn't take the course, though I'm very much open to alternate hypotheses.

36. Mentionsmark_l_watson2019in “Deep Learning with PyTorch”

    I have used TensorFlow almost exclusively for my deep learning work in the last five years and using TensorFlow is much more common, largely I think, because Andrew Ng’s excellent five part course used it. On the other hand Jerry Howards’s wonderful free classes use mostly PyTorch so maybe my argument does not hold.

    For research and for use in published papers, PyTorch is much more widely used.

37. Mentionsunoti2019in “ML From Scratch, Part 1: Linear Regression”

    It does exist! There are lots of answers to what you’re asking for. But here’s the best answer:

    https://course.fast.ai/

    This course uses python and pytorch and will have you building something that can tell 30+ breeds of cats and dogs apart in the first lesson. While the old course from Andrew ng you mentioned starts with theory and by the end gets to doing something useful, the fastai course starts with best practices and moves into the underlying theory later.

    Also Andrew Ng has a newer deeplaearning.ai course on coursera that is all python and doesn’t do Octave.

38. Mentionsrdrey2018in “Organized Resources for Deep Learning Researchers and Developers”

    I've done Andrew Ng's Coursera specialization (deeplearning.ai) and course.fast.ai, and I would 100% recommend starting with fast.ai. (Seeing results more quickly is motivating. It's also free.) When you know that you enjoy the topic, feel free to learn more rigorous ML theory from other sources.

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