Source: Machine Learning Grocery Store This article is approximately 1800 words long and suggests a reading time of 10 minutes. One of the most challenging parts of the ML workflow is finding the best hyperparameters for the model. The performance of ML models is directly related to hyperparameters. Introduction Wikipedia states, “Hyperparameter optimization or tuning … Read more
Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, they ultimately boil down to solving optimization problems. Therefore, optimization methods play a central role in the derivation and implementation of machine learning algorithms. In this article, I will provide a comprehensive summary of the optimization algorithms used in machine … Read more
Introduction For almost all machine learning algorithms, whether supervised learning, unsupervised learning, or reinforcement learning, the final goal generally boils down to solving an optimization problem. Therefore, optimization methods occupy a central position in the derivation and implementation of machine learning algorithms. In this article, the author will provide a comprehensive summary of the optimization … Read more
ABSTRACT Abstract In order to meet the intelligent production needs of blast furnaces, Shandong Steel Group Rizhao Co., Ltd. equipped two 5100m³ blast furnaces with a set of blast furnace expert systems. To align the expert system closely with production, the project team optimized and adjusted the parameters of each model in the expert system … Read more
Click the above “MLNLP” to select “Star” public account Important content delivered at the first time From | Zhihu Author | Yao Shi Address | https://zhuanlan.zhihu.com/p/63500952 Column | The Path of Learning Non-Convex Optimization Editor | Machine Learning Algorithms and Natural Language Processing [TensorFlow] Source Code Analysis of AdamOptimizer The optimizers in TensorFlow basically inherit … Read more
Source: DeepHub IMBA This article is approximately 3700 words long, recommended reading time is over 10 minutes. This article will introduce four advanced optimization techniques that may outperform traditional methods in certain tasks, especially when faced with complex optimization problems. In the field of deep learning, the choice of optimizer is crucial for model performance. … Read more
Click the "Little White Learning Vision" above, select to add "Star" or "Top" Important content delivered first Master these 17 methods to accelerate your PyTorch deep learning training in the most effortless way. Recently, a post on Reddit has gone viral. The topic is about how to speed up PyTorch training. The original author is … Read more
Introduction The most common application of the Hessian matrix is in the Newton optimization method, which mainly seeks the extremum points of a function where the first derivative is zero. This article provides a straightforward summary of the two applications of the Hessian matrix in the XGBoost algorithm, namely the minimum child weight algorithm and … Read more
Click on the above“Beginner’s Guide to Vision” to choose to add a Star Mark or “Top” Important content delivered promptly Introduction The most common application of the Hessian matrix is in the Newton method optimization algorithm, which primarily seeks the extrema of a function where the first derivative is zero. This article provides a clear … Read more
XGBoost utilizes a second technique which is second-order optimization, expanding the loss function l(x,y) using a Taylor series expansion. To approximate it to the second order. This is relatively unique in the XGBoost algorithm, differing from the approach of optimizing using gradient descent in GBT, and also different from Adaboost which increases the weights of … Read more