The answer is yes.
A lot of people want to learn machine learning these days. But the daunting bottom-up curriculum that most ML teachers propose is enough discourage a lot of newcomers.
AI's are making waves once again fueled by consciousness claim hype. Before we head into another AI winter, let's try to reduce the hype (just enough to still get funding and calm everyone expectation).
Some concepts in machine learning may at first glance look extremely opaque and difficult to grasp. Yet, most are pretty accessible and simple. However, they are usually clouded by terminology you don't understand or heavy math notations. When you are faced with one such concept that is stumping you: 1.
To ensure high quality data science project are produced by your team, incorporating code reviews in your workflow is a must!
Being able to quantify the amount of information in a sequence is important in many fields and applies to many data set. Shannon Entropy is one such information theory method that given a random variable and historic about this variable occurrence can quantify the average level of information!
Generating art with AI is surprisingly easy! Here are 5 links to get you started!
This week I'll be sharing 5 books on Machine Learning that I highly recommend. No particular order, I read all of them and they were truly helpful throughout my PhD!
I remember when I first started doing research, I always felt bad and stressed whenever I generated a negative results. My thought process was that I must have messed up something along the way and that I wasn't good enough.
All assumptions, especially those underlying the project creation, should be documented and fact checked!