<story><title>Machine Learning: Models with Learned Parameters</title><url>https://indico.io/blog/simple-practical-path-to-machine-learning-capability-part3/</url></story><parent_chain></parent_chain><comment><author>antirez</author><text>I strongly advise everybody with one day free (and not much better things to do) to implement a basic fully connected feedforward neural network (the classical stuff, basically), and give it a try against the MNIST handwritten digits database. It&amp;#x27;s a relatively simple project that learns you the basic. On top of that to understand the more complex stuff becomes more approachable. To me this is the parallel task to implement a basic interpreter in order to understand how higher level languages and compilers work. You don&amp;#x27;t need to write compilers normally, as you don&amp;#x27;t need to write your own AI stack, but it&amp;#x27;s the only path to fully understand the basics.&lt;p&gt;You&amp;#x27;ll see it learning to recognize the digits, you can print the digits that it misses and you&amp;#x27;ll see they are actually harder even for humans sometimes, or sometimes you&amp;#x27;ll see why it can&amp;#x27;t understand the digit while it&amp;#x27;s trivial for you (for instance it&amp;#x27;s an 8 but just the lower circle is so small).&lt;p&gt;Also back propagation is an algorithm which is simple to develop an intuition about. Even if you forget the details N years later the idea is one of the stuff you&amp;#x27;ll never forget.</text></comment>
<story><title>Machine Learning: Models with Learned Parameters</title><url>https://indico.io/blog/simple-practical-path-to-machine-learning-capability-part3/</url></story><parent_chain></parent_chain><comment><author>nkozyra</author><text>This is well-written and I applaud any step toward demystifying the sometimes scary sounding concepts that drive much of the ML algorithms.&lt;p&gt;Knowing you can pretty quickly whip up a KNN or ANN in a few hundred lines of code or fewer is one of the more eye-opening parts of the delving in.  For the most part, supervised learning follows a pretty reliable path and each algorithm obviously varies in approach, but I know I originally thought &amp;quot;deep learning?  ugh, sounds abstract and complicated&amp;quot; before realizing it was all just a deep ANN.&lt;p&gt;Long story short: dig in.  It&amp;#x27;s unlikely to be as complex as you think.  And if you&amp;#x27;ve ever had an algorithms class (or worked as a professional software dev) none of it should be too daunting.  Your only problem will be keeping up the charade if people around you think ML&amp;#x2F;AI is some sort of magic.</text></comment>