Robot Updated at:22 Feb 2025 21:10:43 GMT

Author: yiliuyan161

docs/awesome/awesome-agi-cocosci.md

@@ -290,7 +290,7 @@ Contributions are greatly welcomed! Please refer to [Contribution Guidelines](ht
 
 * [Taking the Human Out of the Loop: A Review of Bayesian Optimization](https://ieeexplore.ieee.org/abstract/document/7352306) - ***Proceedings of the IEEE***, 2015. [[All Versions](https://scholar.google.com/scholar?cluster=2039456143890648437)]. [[Preprint](https://www.cs.princeton.edu/~rpa/pubs/shahriari2016loop.pdf)]. Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game engines, speech recognizers) thus involve many tunable configuration parameters. These parameters are often specified and hard-coded into the software by various developers or teams. If optimized jointly, these parameters can result in significant improvements. Bayesian optimization is a powerful tool for the joint optimization of design choices that is gaining great popularity in recent years. It promises greater automation so as to increase both product quality and human productivity. This review paper introduces Bayesian optimization, highlights some of its methodological aspects, and showcases a wide range of applications.
 
-* [Practical Bayesian Optimization of Machine Learning Algorithms](https://proceedings.neurips.cc/paper/2012/file/05311655a15b75fab86956663e1819cd-Paper.pdf) - ***NeurIPS'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=14442949298925775705&hl=en&as_sdt=0,5)]. The original paper for applying Bayesian optimization to machine learning hyperparameter selection.
+* [Practical Bayesian Optimization of Machine Learning Algorithms](https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html) - ***NeurIPS'12***, 2012. [[All Versions](https://scholar.google.com/scholar?cluster=14442949298925775705)]. The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a “black art” requiring expert experience, rules of thumb, or sometimes brute-force search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. This work considers this problem through the framework of Bayesian optimization, in which a learning algorithm’s generalization performance is modeled as a sample from a Gaussian process (GP). The authors show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expert-level performance. The authors describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. These proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including Latent Dirichlet Allocation, Structured SVMs and convolutional neural networks.
 
 * [A Tutorial on Bayesian Optimization](https://arxiv.org/abs/1807.02811) - 2018. [[All Versions](https://scholar.google.com/scholar?cluster=7971934771645047583&hl=en&as_sdt=0,5)]. 
 

docs/awesome/awesome-angular.md

@@ -563,6 +563,7 @@ become an Angular expert.
 * [ngx-pwa](https://github.com/Service-Soft/ngx-pwa) - Provides additional functionality around Angular pwa's. Most notably being able to cache and sync POST/PATCH/DELETE Requests.
 * [ngx-repository](https://github.com/paddls/ngx-repository) - Easily create a strongly typed data client (HTTP REST or Firestore) in your Angular project.
 * [ng-rest-client](https://github.com/gizm0bill/gzm/tree/master/libs/ng-rest-client) - This library provides a set of decorators for simplifying HTTP requests. It enables developers to define RESTful API clients using decorators for common HTTP methods.
+* [ngx-http-helper](https://github.com/InnovA2/ngx-http-helper) - A lightweight library to easily call your APIs and add JWT token or API key on each header request.
 
 #### Integrations
 
@@ -619,6 +620,7 @@ become an Angular expert.
 * [ngx-reactify](https://github.com/knackstedt/ngx-reactify) - Library to make running Angular and React applications together easy.
 * [Otter](https://github.com/AmadeusITGroup/otter) - A highly modular framework whose goal is to provide a common platform to accelerate and facilitate the development on Angular web applications. It is split into several units to cover different aspects of these applications (localization, testing, customization, etc.). Also, to customize an application, metadata can be extracted from the application source code and injected into a CMS to manage dynamic configuration.
 * [ngx-serializer](https://github.com/paddls/ngx-serializer) - Angular wrapper of @paddls/ts-serializer library.
+* [ngx-pocketbase](https://github.com/BerniHC/ngx-pocketbase) - PocketBase Angular SDK for interacting with the [PocketBase API](https://pocketbase.io/docs). Based on the [PocketBase JavaScript SDK](https://github.com/pocketbase/js-sdk).
 
 #### Internationalization
 
@@ -1625,6 +1627,7 @@ to simplify usage and allow quick customization.
 * [ng-verse](https://github.com/lukonik/ng-verse) - A collection of feature-rich Angular components, directives, and pipes. Unlike traditional UI libraries, it requires no installation—just copy and paste what you need into your project.  Check the [docs](https://www.ng-verse.dev/) for more.
 * [primitives](https://github.com/radix-ng/primitives) - Angular port of [Radix UI](https://www.radix-ui.com/) Primitives. Accessible. Customizable.
 * [xUI](https://github.com/Rikarin/xui) - Angular UI Component Library heavily inspired by Discord design.
+* [bryntum](https://bryntum.com/) - World class web components for calendars, gantt charts, kanban boards, and scheduling.
 
 ##### Material Based