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      "author": "setsunai",
      "body": "Seriously. Thank you so much. All of this info is unbelievably helpful and exactly the kind of direction I've been looking for. I started khan academy courses for multivariable calc and linear algebra for a refresher last week and this outline will help me immensely with my next steps.  I just heard about Fast.ai in this panel conversation between Vivienne Ming, Jeremy Howard, and Peter Diamandis yesterday, and am excited to take the course. Dr. Ming's work has been super inspiring for me. \n\nThanks again for taking the time to respond so thoroughly! I will definitely take you up on that as questions come up through my studies :) \n\n-Richard",
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      "body": "Hey, thanks for the comments!\n\nGANs have definitely been achieving some unbelievable results and will for sure continue to be one of the most promising areas in ML in 2018. In addition,  I personally think that we'll also see a lot of great work this year in: unsupervised/self-supervised learning, exploration, attention, and evolutionary strategies. \n\nIt's definitely not too late at all to move into ML! One of the best things about ML is the (relatively) low barrier to entry. In most other fields, in order to push the bleeding edge, you need to study decades of theory in order to reach the boundary of what's been explored. In ML, it seems that you can reach that point much faster, thanks to the wealth of accessible online materials and immense levels of open research communication.\n\nSo... where to start? I personally don't have experience with Udacity's nanodegree programs, but I've looked at the syllabus before and it seems reasonable. What I've seen a lot of people do is Andrew Ng's famous [Coursera class](https://www.coursera.org/learn/machine-learning), which is an online version of Stanford's introductory ML course.\n\nPersonally, I would do something like:\n\n1. Make sure I have a strong grasp of the fundamental prerequisites [Linear algebra, multivariable calculus, some mathematical maturity, Python]. If you are missing any of these, a few weeks diving deep would probably be good enough to pick up whatever else you need as you go.\n2. Take a course or two to get a structured overview of important topics in machine learning and deep learning. In addition the the Udacity program, I would also consider the Coursera class I mentioned above and the [Fast.ai course](http://www.fast.ai/). \n3. Consider a textbook or two. [This](http://www.deeplearningbook.org/) is a classic.\n4. Projects projects projects! The best way to learn (and show others what you know) is to work on ML projects. Whether you choose to contribute to open source projects or work on your own, I think having a strong portfolio is one of the most important things to do as a CS/ML outsider trying to break in. These will speak far more than course certificates, in my opinion.\n\nRegardless of what path you choose though... it's definitely not too late! ML is definitely here to stay. Calling it [Software 2.0](https://medium.com/@karpathy/software-2-0-a64152b37c35) might be slight hyperbole, but I have no doubt that ML systems (and people who work with them!) will be very crucial to how our society works in the next decades.\n\nAnd yes, Google Brain is amazing. I love going to work every day with such smart colleagues and working on problems I care about.\n\nGenerative models for architectural design is definitely a very interesting problem! I'm not aware of any work that approaches anywhere near what you are proposing. I think a lot of the challenges stem from the very very complex dynamics that go into ensuring a building is structurally safe; while we may be far away from an end-to-end system that can output detailed, architecturally meaningful blueprints that are safe and useful, I think it is very plausible that deep learning systems can at least automate some mundane parts of the architectural design process. Which could very much drive prices down 10x in the future :)\n\nHope that was helpful! Happy to answer any other questions.",
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      "body": "Hi! \n\nI am extremely interested in machine learning and AI, but am very much a novice when it comes to the academic and technical side of things. I am particularly interested in how human creativity can be augmented with ML and AI. The progress made over the last year with GANs has been super exciting to me. Also the latest Tacotron 2 (think that was what it was called) paper from google was another recent AI/ML event that blew my mind.\n\n Soooo yeah literally everything and anything when it comes to content would be amazing! (GANs, NLP, reinforcement learning especially!)\n\nContent with advice pertaining to how someone with education outside of computer science could work their way into the industry would be amazing and seriously helpful as well. I personally have a BS in Biology/BA in English and am currently in medical school in South Dakota, but my passion really lies in AI/ML and Architecture/Design. I am considering changing my path to a career in ML/AI, but am uncertain about how to go about making the shift. I am 25 already, but really feel my energy could be put to better use in a field like ML that I am truly passionate about, but I am a little nervous about maybe being a little too late to the party and not having the \"right\" background to get into the programs that would provide me this opportunity.\n\nI have recently started teaching myself python through audacity and am hoping to work through their introductory machine learning programs this spring. Is this a good place to start in your opinion? Are non-traditional online education opportunities like audacity's nanodegrees really a feasible way to move towards a career as a ML engineer without a Bachelor's degree or MS in computer science/engineering? At this point is this just a pipe dream lol?\n\nSorry for the long and poorly organized reply lol. Really looking forward to your content, whatever you decide to do!",
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      "author": "frank1in",
      "permlink": "re-txizzle-introducing-myself-20171208t073014302z",
      "voter": "txizzle",
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  "op_in_trx": 0,
  "timestamp": "2017-12-08T07:36:57",
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{
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{
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{
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  "op": [
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    {
      "author": "che-pup",
      "body": "Hello and welcome to steemit.\nI joined in July but only started posting last month.\n\nOne thing I have found is that it is difficult to get followers and therefore have your posts seen and upvoted. If I may reccommend something to you it's steemfollower. basically it's just a much smaller subcommunity where you can find followers and we all upvote within that community.\n\nIt doesn't cost anything and participating on steemfollower has no impact on your profile here except in the normal manner of upvoting.\n\nFeel free to check it out via my referral.\n\nhttps://steemfollower.com/?r=1716\n\n(All steemfollowers receive a bonus for referrals).\n\nGood luck in your steem voyage!",
      "json_metadata": "{\"tags\":[\"introduceyourself\"],\"links\":[\"https://steemfollower.com/?r=1716\"],\"app\":\"steemit/0.1\"}",
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{
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      "author": "frank1in",
      "body": "Hello) good luck",
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      "parent_author": "txizzle",
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