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Dive into Deep Learning
- throwlaplace 7y agothis looks pretty good. certainly much better than goodfellow's deep learning book. definitely much the diagrams and code are much appreciated but i'm curious why mxnet over pytorch?
- sanjose321 7y agoI find this comment amusing, have you read the goodfellow's book? That book is amazing.
- throwlaplace 7y agoi read the first half of it very closely and skimmed the second.
- deleted 7y ago[deleted]
- hnarayanan 7y agoI suppose you and I have very different notions of the word 'amazing'.
- bensaccount 7y agoall the authors look to be Amazon employees and I think MXNet is Amazon's "chosen" DL framework.
- throwlaplace 7y agoah that makes sense. should've googled author's names. i just assumed they were academic because of the large number of unis using the book.
- deleted 7y ago[deleted]
- dragandj 7y agoI'll chip in with my book, which is written with programmers in mind, implements everything from scratch, works on CPU and GPU, at great speed. Directly links theory to implementation, and you can use it along with Goodfellow's Deep Learning book. Also, discusses all steps, and does not skip gradients by using autograd. Deep Learning for Programmers: An Interactive Tutorial with CUDA, OpenCL, DNNL, Java, and Clojure. https://aiprobook.com/deep-learning-for-programmers/ https://aiprobook.com/deep-learning-for-programmers/
- vga805 7y agoAnd, what makes me want to dive into this the most, there's some Clojure! Will definitely have to take a look a this one. Thanks.
- dragandj 7y agoThere's lots of Clojure! (in relative terms. In absolute terms, there's not much of it because Clojure is so concise and powerful that everything is implemented with very little code :)
- mpfundstein 7y agoIs there a print version (in the planning)? I usually don’t buy ebooks
- dragandj 7y agoOnly a limited hand-crafted hardcover edition is planned. That being said, you can print a dead tree version from the PDF at your local printing shop (or at home) if you care about the text, and not that much about binding.
- mpfundstein 7y agoYes, that would be an option.
- sanxiyn 7y agoSee also Dive into Deep Learning Compiler from the same team: http://tvm.d2l.ai/ http://tvm.d2l.ai/
- fareesh 7y agoAs an engineer I find myself in this type of situation quite often - if anyone can point me to some good resources or has any advice, I'd be quite grateful: - Some non-technical stakeholder comes to me and says "can we solve this problem with Machine Learning?" usually it's something like "there need to be two supervisors on the factory floor at all times, and I want an email alert everytime there are less than 2 supervisors for more than 20 minutes" - I ask for some sample footage to build a prototype and get a few very poor quality videos, at a very different standard from what I see in most of these tutorials. - I find some pre-trained model that is able to do people detection or face detection and return bounding rectangles and download it in whatever form - After about 30 minutes of fiddling and googling errors, I run it against the sample footage - I get about 60% accuracy - this is no good. Where do I go from here? Keep trying different models? There are all sorts of models like YOLO and SSD and RetinaNet and YOLO2 and YOLO3. - At some point I try a bunch of models and all of them are at best 75% good. At this point I figure I should train it with my own dataset, and so I guess I need to arrange to have this stuff labelled. In my experience stakeholders are usually willing to appoint someone to do it but they want to know how much footage they need to label and whether their team will need special training to do the labelling and after it's all done is this even going to work? What are some effective / opinionated workflows for this part of the overall process that have worked well for you? What's a labelling tool that non-technical users can use intuitively? How good are tools/services like Mechanical Turk and Ground Truth? This part of the process costs time and money - stakeholders, particularly managers who are non-technical tend to want an answer beforehand - "If we spend all this time and money labelling footage, how well is this going to work? How much footage do we need to label?". How do you handle these kinds of conversations? I find this space fairly well-populated with ML tutorials and resources but haven't been able to find content that is focused on this part of the process.
- carbocation 7y ago60% on a per-frame basis might be enough if all you need to do is identify the condition "two supervisors are not on the floor" for at least 20 minutes. As in, if you compute your per-frame score and compare it over bigger chunks of time, is it sufficiently different when 2 are on the floor and 2 are not?
- dang 7y agoDiscussed a year ago: https://news.ycombinator.com/item?id=18838808 https://news.ycombinator.com/item?id=18838808
- whoisnnamdi 7y agoGreat guide - though unless I missed it I think this is missing the latest advancements around Transformers, BERT, ELMo, etc. This stuff is pretty fresh, so it's understandable, but the NLP chapter would be greatly enhanced by covering these newer topics
- enitihas 7y agoIs there any book which has more than a passing mention of BERT?
- kolleykibber 7y agoRFID at the doors?
- whoevercares 7y agoDoes MxNet as a DL framework still have a place given Pytorch/tensorflow pretty much dominated all use cases?Amazon/AWS still “officially” supported it but given its product driven culture it could replace it with whatever framework that move faster and is more demanded by customers. Vendor Lock-in in this case probably won’t work as well since Amazon is not quite a leader in this case
- samcodes 7y agoMXNet existed before AWS picked it, and it has a lot of strengths. I’d use it (especially with Gluon) over TF any day. But that said, PyTorch is usually easy to use on AWS... the preference for MXNet seems weak
- thatsenough 7y agoIt existed at CMU, but it seems like even CMU has moved over to PyTorch. I think Amazon just doesn't want to seem like an "also ran" by conceding to one of its competitor's frameworks.
- bor100003 7y agoHas anyone read this book ? It look very attractive but I want to hear some feedback before bookmarking another ML book.
- lindbergh 7y agoKinda did, but mostly the first chapters, actually up to CNN chapter (where real modern DL start). But so far, I really liked what I read. It has a very good blend of code and theory, with hands on applications throughout the whole book. Most importantly, all those applications could perfectly be copy pasted into your own environment. So it actually reminded me of a very thorough tutorial on a framework, more say than a regular textbook, although the authors don't compromise on mathematical arguments (but don't get lost in it either, they skimmed pretty fast on regularization theory imho). If you've had previous exposure to classical ML, I think it's a fantastic introduction to DL, enough to get started.