虫虫首页|资源下载|资源专辑|精品软件
登录|注册

Decisions

  • Telecom Frontends

    The ability to analyze system or circuit behavior is one of the key requirements for successful design. To put an idea to work, a designer needs both the knowledge and tools for analyzing the behavior of that new system architecture or that experi- mental circuit topology. Design Decisions are grounded on the results obtained from analysis.

    标签: Frontends Telecom

    上传时间: 2020-06-01

    上传用户:shancjb

  • Auto-Machine-Learning-Methods-Systems-Challenges

    The past decade has seen an explosion of machine learning research and appli- cations; especially, deep learning methods have enabled key advances in many applicationdomains,suchas computervision,speechprocessing,andgameplaying. However, the performance of many machine learning methods is very sensitive to a plethora of design Decisions, which constitutes a considerable barrier for new users. This is particularly true in the booming field of deep learning, where human engineers need to select the right neural architectures, training procedures, regularization methods, and hyperparameters of all of these components in order to make their networks do what they are supposed to do with sufficient performance. This process has to be repeated for every application. Even experts are often left with tedious episodes of trial and error until they identify a good set of choices for a particular dataset.

    标签: Auto-Machine-Learning-Methods-Sys tems-Challenges

    上传时间: 2020-06-10

    上传用户:shancjb

  • interpretable-machine-learning

    Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their Decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.

    标签: interpretable-machine-learning

    上传时间: 2020-06-10

    上传用户:shancjb