https://markokello.com/blog en-us () () Tue, 06 Aug 2019 00:00:00 GMT https://markokello.com/blog/classical-ml https://markokello.com/blog/classical-ml This blog is a follow-up to a presentation I gave at Outbox Hub, Its self-contained and explains the mathematics and theory behind key classical machine learning algorithms. Tue, 06 Aug 2019 00:00:00 GMT () MLStatistics https://markokello.com/blog/derivatives https://markokello.com/blog/derivatives . Wed, 30 Oct 2019 00:00:00 GMT () MLMathsDeep Learning https://markokello.com/blog/gradient-descent https://markokello.com/blog/gradient-descent G.D is an iterative optimization algorithm for training Machine Learning models with the primary purpose of finding the optimal parameters (weights and biases) for the model. The gradient of the loss function, also known as a vector of partial derivatives, indicates the steepest direction of increase. It is then repeatedly updated by taking a step in the opposite direction. The size of the step is controlled by the learning rate. And through this process, the algorithm gradually drives the loss value lower until it converges toward a local minimum. Tue, 19 Nov 2019 00:00:00 GMT () MLDeep Learning https://markokello.com/blog/information-theory https://markokello.com/blog/information-theory Entropy measures the level of uncertainty or randomness in a dataset. Information gain, in turn, evaluates how effectively a decision tree split reduces this entropy. It measures the reduction in uncertainty achieved by a particular split, helping to identify which features create the most meaningful divisions in the data and lead to better classification decisions. Mon, 23 Sep 2019 00:00:00 GMT () MLDeep Learning https://markokello.com/blog/probability-distributions https://markokello.com/blog/probability-distributions Fri, 15 May 2020 00:00:00 GMT () MLStatisticsMathsDeep Learning https://markokello.com/blog/probability-theory https://markokello.com/blog/probability-theory TDA. Tue, 17 Dec 2019 00:00:00 GMT () MathsStatisticsML