[HN Gopher] Intro to Deep Learning
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       Intro to Deep Learning
        
       Author : tbau50
       Score  : 97 points
       Date   : 2021-08-12 13:55 UTC (9 hours ago)
        
 (HTM) web link (sebastianraschka.com)
 (TXT) w3m dump (sebastianraschka.com)
        
       | atum47 wrote:
       | Thank you for sharing. I'll make sure to watch it.
        
       | lol1lol wrote:
       | can someone please write an advanced deep learning book. there
       | are too many intros.
        
       | firefoxd wrote:
       | It was kinda hard for me to understand the basics of deep
       | learning. A lot of formal courses makes a lot of assumptions.
       | 
       | I ended up watching the deep learning series by 3blue1brown,
       | reading the book it was based on. Watching lots of linear algebra
       | and calculus videos. Then taking the deep learning specialization
       | program on Coursera. This process took 2 months, it taught me the
       | basics.
       | 
       | Then I built my own project and realized there were lots wholes
       | in my knowledge base. I found an old post on hn that recommended
       | deeplizard on YouTube, that was an excellent recommendation and
       | intro to pytorch.
       | 
       | One thing to keep in mind is that as you are learning, new
       | methods are being invented.
       | 
       | Edit: typos
        
         | mdp2021 wrote:
         | Sorry, speaking of web-based books about Deep Learning, I
         | remember one in which the figures allowed changing parameters
         | and seeing the results - I cannot remember the source or
         | author, can anyone?
         | 
         | I confused it with the one from Michael Nielsen...
        
         | amenod wrote:
         | I went through multiple resources on the net until it clicked
         | for me. I guess each book / video / blog post gives you the
         | bits you are ready for, and when enough of these bits
         | accumulate you understand it. Looking back, neural networks are
         | not that difficult to grasp, but there are many important
         | details that one should understand.
         | 
         | What I learned is that it is very important to test
         | intermediate results thoroughly. Nets will gladly learn from
         | garbage if that is what you (unknowingly) feed them, and they
         | might even produce some results - not at acceptable level
         | though.
         | 
         | I didn't continue working in this field, but I'm happy I
         | learned it (and used it in a project too) and would recommend
         | that to any developer. ML really moves the frontier of what we
         | can achieve in software much further.
        
         | iamcreasy wrote:
         | > I ended up watching the deep learning series by 3blue1brown,
         | reading the book it was based on.
         | 
         | What book you are referring to?
        
           | firefoxd wrote:
           | This one http://neuralnetworksanddeeplearning.com
           | 
           | It goes much deeper then the videos. Also it is free.
        
             | atum47 wrote:
             | Great
        
             | max_ wrote:
             | That book is excellent. It's the only one that I can
             | recommend to beginners.
        
       | candlemas wrote:
       | There sure are a lot of introductions to deep learning.
        
       | malshe wrote:
       | Sebastian is also the author of "Python Machine Learning"
       | https://www.amazon.com/dp/1789955750/
        
         | yuy910616 wrote:
         | I would add that this is one of the few good books by Packt.
         | Bayesian Analysis with Python is also pretty good.
         | 
         | Still, most of the time Packt is a reliable signal for low
         | quality
        
           | manishsharan wrote:
           | >>most of the time Packt is a reliable signal for low quality
           | 
           | In my opinion, Packt is the first one with a book on a new
           | technology or framework or use case. When I was getting
           | started with Clojure , I ran into a bunch of problems in
           | applying it. Packt has more clojure data science books than
           | any other source and they were pretty helpful to me. They
           | were not as fundamental to my learning as "Clojure for the
           | Brave.." but ideas from those books helped me on.
           | 
           | edit : formatting.
        
           | wirthjason wrote:
           | That was a good book. I looked at it recently and it's now in
           | the 3rd edition! Congrats.
           | 
           | Any other suggestions for good Packt books?
           | 
           | I agree, Packt's quality is much lower than other publishers.
           | As a rule of thumb I stay away but occasionally there's a
           | gem.
           | 
           | I've been looking at "Machine Learning for Algorithmic
           | Trading". It feels like a dump of wikipedia and a bunch of
           | jupyter notebooks with sloppy code. I cannot decide if it's
           | worth the pain if slogging through that mess.
           | 
           | https://www.amazon.com/Machine-Learning-Algorithmic-
           | Trading-...
        
       | nafizh wrote:
       | If you want just one book to learn all things deep learning from
       | scratch with coding including the latest techniques, it should be
       | Dive into deep learning [0]. Really underrated and not well-
       | known.
       | 
       | [0] https://d2l.ai/
        
         | ganSo wrote:
         | Wow. Did I miss something or is all the content there free?
         | That's awesome
        
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       (page generated 2021-08-12 23:01 UTC)