|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/numerical-optimization-for-mldl-practical-work-1/
课程评论：没有评论
课程名称：机器学习与深度学习优化器实现 课程概述： 在本课程中，您将学习： 1. 如何实现批量（基础）梯度下降（GD）优化器，以获得单变量和多变量线性回归（LR）模型的最佳模型参数。 2. 如何对单变量和多变量LR模型实现小批量和随机GD。 课程将通过附带的笔记本中的指导步骤进行引导，并且还包含视频系列来详细描述每个步骤。您还将实现成本函数、停止条件，并绘制学习曲线。您将理解向量化实现优化器的强大功能。这种实现将帮助您巩固概念，并深入理解优化器在训练阶段的工作原理。 通过本课程，您将获得理论和实践角度的平衡，了解在机器学习（ML）和深度学习（DL）中广泛应用的优化器。我们将重点讲解ML和DL中使用的主要数值优化概念和技巧。尽管我们将这些技术应用于单变量和多变量LR，但其概念适用于其他ML和DL模型。我们在这里使用LR来简化问题，专注于优化器而非模型。 在后续的实际操作中，我们将扩大视野，实现更高级的优化器，例如： - 基于动量的GD - Nesterov加速梯度（NAG） - 自适应梯度法（Adagrad） - RMSProp - Adam - BFGS 您将获得以下材料： - 数值优化的课程材料（幻灯片），帮助您在5天内掌握机器学习和深度学习的基础。 - 要遵循的指导步骤的笔记本。 - 实际工作理想解决方案的笔记本（实现）。 - 数据文件。 您应该独立进行实现，并将您的代码与单独笔记本中提供的实际解决方案进行比较。提供了一个解释解决方案的视频系列，但在完成自己的实现之前，请不要查看解决方案。
In this course you will learn:1- How to implement batch (vanilla) gradient descent (GD) optimizer to obtain the optimal model parameters of the single and Multi variable linear regression (LR) models.2- How to implement mini-batch and stochastic GD for single and multi-variable LR models.You will do this by following the guided steps represented in the attached notebook.In addition a video series describing each step.You will also implement the cost function, stop conditions, as well as plotting the learning curves.You will understand the power of applying vectorize implementation of the optimizer.This implementation will help you to solidify the concept and gain the momentum of how the optimizers work during training phase.By the end of this course you will obtain the balance between the theoretical and practical point of view of the optimizers that is used widely in both machine learning (ML) and deep learning (DL).In this course we will focus on the main numerical optimization concepts and techniques used in ML and DL.Although, we apply these techniques for single and multivariable LR, the concept is the same for other ML and DL models.We use LR here for simplification and to focus on the optimizers rather than the models. In the subsequent practical works we will scale this vision to implement more advanced optimizers such as:- Momentum based GD.- Nestrov accelerated gradient NAG.- Adaptive gradient Adagrad.- RmsProp.- Adam.- BFGS.You will be provided by the following:- Master numerical optimization for machine learning and deep learning in 5 days course material (slides).- Notebooks of the guided steps you should follow.- Notebooks of the practical works ideal solution (the implementation).- Data files.You should do the implementation by yourself and compare your code to the practical session solution provided in a separate notebook.A video series explaining the solution is provided. However, do not see the solution unless you finish your own implementation.
