I firmly believe that this is the best way to study: I will show you the road, but you must walk it. Go deep into a concept that is introduced, then check the roadmap and move on. Instead of reading through it in one sitting, I recommend using this article as a reference point throughout your studies. This way, you will be able to study other topics without difficulties, if need be. Instead, we will focus on getting our directions. To keep things simple, the aim is not to cover everything. ![]() This post aims to present a roadmap of all the mathematics for machine learning, taking you from absolute zero to a deep understanding of how neural networks work. With proper foundations, though, most ideas can be seen as quite natural. If you are a beginner and don't necessarily have formal education in higher mathematics, creating a curriculum for yourself is hard. Understanding methods like stochastic gradient descent might seem challenging since it is built on top of multivariable calculus and probability theory. However, most of this knowledge is hidden behind layers of advanced mathematics. ![]() This is especially true when you want to push the boundaries of state-of-the-art deep learning tools. If you have ever built a model for a real-life problem, you probably experienced that familiarity with the details goes a long way if you want to move beyond baseline performance. Knowing the mathematics behind machine learning algorithms is a superpower.
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