https://nyghtowl.com/tags/talks/ Recent content in Talks on Nyghtowl Hugo en-us <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="noopener">CC BY-NC-SA 4.0</a> Sun, 12 Apr 2015 14:16:42 +0000 https://nyghtowl.com/posts/2015/04/pycon-2015-neural-nets-for-newbies/ Sun, 12 Apr 2015 14:16:42 +0000 https://nyghtowl.com/posts/2015/04/pycon-2015-neural-nets-for-newbies/ <p>The ideas and methods in neural nets (NNs) have been around for a long time, but in the last decade plus, we are finally starting to reap significant benefits, and this is just the beginning. This post provides an overview of my recent PyCon talk in Montreal which is a neural net primer of sorts. The video is below, my slides are on <a href="https://speakerdeck.com/nyghtowl/neural-nets-for-newbies">SpeakerDeck</a>, and I have a repo on Github named <a href="https://github.com/nyghtowl/Neural_Net_Newbies">Neural Nets for Newbies</a>.</p> https://nyghtowl.com/posts/2014/04/pycon-2014-how-to-get-started-with-machine-learning/ Sun, 13 Apr 2014 23:14:26 +0000 https://nyghtowl.com/posts/2014/04/pycon-2014-how-to-get-started-with-machine-learning/ <p>Following up on the talk I just gave at PyCon 2014 in Montreal, I’ve explained parts of my presentation and provided a few additional clarifications. You can catch the talk at <a href="http://pyvideo.org/video/2604/how-to-get-started-with-machine-learning">Pyvideo.org</a>, my <a href="https://github.com/nyghtowl/PyCon_2014">github repo PyCon2014</a> holds the sample code, and the <a href="https://speakerdeck.com/nyghtowl/how-to-get-started-with-machine-learning">slides</a> are on SpeakerDeck.</p> <p><strong>Machine Learning (ML) Overview</strong></p> <p>Arthur Samuel defined machine learning as, “Field of study that gives computers the ability to learn without being explicitly programmed”. Its about applying algorithm(s) in a program to solve the problem you are faced with and address the type of data that you have. You create a model that will help conduct pattern matching and/or predict results. Then evaluate the model and iterate on it as needed to create the right type of solution for the problem.</p>