10.[Deep Learning] 常用的Active functions & Optimizers 积分与排名 积分- 217623 排名- 5418 随笔分类 Algorithm(34) Bash(1) C/C++(6) Computational Advertising(1) Data Structure(6) Database(3) Evolutionary Algorithm(2) Hadoop(4) Linux(6) Machine Learning(25) Math(2) Net...
Maximum-likelihood estimationis a common learning algorithm used by a variety of machine learning algorithms, although it does make assumptions about the distribution of your data (more on this when we talk about preparing your data). The best coefficients would result in a model that would predic...
Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. In this post you will lear...
Logistic regression is a powerful and interpretable classification algorithm widely used in machine learning. Understanding its sigmoid function, cost function, assumptions, and implementation equips you to apply it effectively in real-world scenarios. If you want to learn about these techniques, then yo...
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduceregularization, which helps prevent models fromoverfittingthe training data. 到现在为止 你已经见识了 几种不同的学习算法包括线性回归和逻辑回归它们能够有效地解决许多问题...
the algorithm (which experiences the data and performs on the task) the performance measure Why does this matter? Regression and classification are types ofmachine learning tasks. Additionally, the structure of the input data (i.e., the “experience” that we use to train the system) is diffe...
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduceregularization, which helps prevent models fromoverfittingthe training data. 到现在为止 你已经见识了 几种不同的学习算法包括线性回归和逻辑回归它们能够有效地解决许多问题...
Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. Our loss function is differentiable indeed, so we can use it to find the local minimum (also the global minimum in this case). Let’s get it by one chart. Gradient ...
Shortcut learningWe cannot even blame the machine learning algorithm, since it performed exactly what we asked it to do: For the majority of samples, it holds good predictive power. It is just that houses exceeding a price of 4 M$ are underrepresented in the dataset – only 4% of the ...
It is a binary classification algorithm used when the response variable is dichotomous (1 or 0). Inherently, it returns the set of probabilities of target class. But, we can also obtain response labels using a probability threshold value. Following are the assumptions made by Logi...