<story><title>Learning Machine Learning: A beginner&apos;s journey</title><url>http://muratbuffalo.blogspot.com/2016/12/learning-machine-learning-beginners.html</url></story><parent_chain></parent_chain><comment><author>saurabhjha</author><text>I think this &amp;quot;machine learning for hackers&amp;quot; approach is just not enough. Oftentimes, you do need a solid theoretical&amp;#x2F;mathematical background. Most people seems to approach ML like they approach programming tools or libraries - learn just enough to get job done and move on.&lt;p&gt;I was studying machine learning from Andrew Ng&amp;#x27;s CS229 (the class videos are online. I think they date from 2008 or hereabout). There is no way you can progress beyond lecture 2 (out of 20) without a solid probability background. A solid background in probability&amp;#x2F;statistics probably means a good first course in Probability or maybe the first five chapters of &amp;quot;Statistical Inference&amp;quot; by Cassias and Berger. Similarly, for SVM, you need a solid background in Linear Algebra and so on. You probably also need a background Linear Optimization. Here are the recommendations by Prof. Michael Jordan &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;news.ycombinator.com&amp;#x2F;item?id=1055389&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;news.ycombinator.com&amp;#x2F;item?id=1055389&lt;/a&gt;&lt;p&gt;Not a lot of people want to dive in this much. They have got things to do and who cares about proofs anyway. The thinking goes like &amp;quot;Most of the mathematics is abstracted away by libraries like scikit-learn. Let&amp;#x27;s get shit done.&amp;quot;. Well, I think a lot of competitive advantage of Google&amp;#x2F;Facebook in ML is because they have staffed their engineering with people who have studied these things for years (by PhD). Compare that to flipkart&amp;#x27;s recommendations.&lt;p&gt;However, I don&amp;#x27;t think this problem is unique to ML&amp;#x2F;Data Science. It is equally bad in &amp;quot;Distributed systems&amp;quot;. Let&amp;#x27;s use Docker, that&amp;#x27;s the future!</text></comment>
<story><title>Learning Machine Learning: A beginner&apos;s journey</title><url>http://muratbuffalo.blogspot.com/2016/12/learning-machine-learning-beginners.html</url></story><parent_chain></parent_chain><comment><author>minimaxir</author><text>&amp;gt; So I am doubling down on ML&amp;#x2F;DL.&lt;p&gt;The amount of free resources now available for learning machine learning&amp;#x2F;deep learning nowadays is robust and easy to comprehend. (indeed, Andrew Ng&amp;#x27;s Coursera class is very good). And running running ML code is even easier, with libraries like Tensorflow&amp;#x2F;Theano to abstract the ML gruntwork (and Keras to abstract the abstraction!)&lt;p&gt;I suspect that there may be machine learning knowledge &lt;i&gt;crash&lt;/i&gt;, where the basics are repeated endlessly, but there is less &lt;i&gt;unique, real world application&lt;/i&gt; of the knowledge learned. I&amp;#x27;ve seen many Internet testimonials saying how &amp;quot;I followed an online tutorial and now I can classify handwritten digits, AI is the future!&amp;quot; The meme that Kaggle competitions are a metric of practical ML skill encourages budding ML enthusiasts to look at minimizing log-loss or maximizing accuracy &lt;i&gt;without considering time&amp;#x2F;cost tradeoffs&lt;/i&gt;, which doesn&amp;#x27;t reflect real-world constraints.&lt;p&gt;Unfortunately, many successful real world applications of ML&amp;#x2F;DL are the ones &lt;i&gt;not&lt;/i&gt; being instructed in tutorials as they are trade secrets (this is the case with &amp;quot;big data&amp;quot; literature, to my frustration). OpenAI is a good step toward transparency in the field, but that won&amp;#x27;t stop the ML-trivializing &amp;quot;this program can play Pong, AI is the future!&amp;quot; thought pieces (&lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;news.ycombinator.com&amp;#x2F;item?id=13256962&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;news.ycombinator.com&amp;#x2F;item?id=13256962&lt;/a&gt;).</text></comment>