综述学习算法在多领域应用，融合深度学习与大模型提升性能。
AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration
- 整合CNN与机器学习构建混合模型，提升特征识别能力。
- 通过大规模数据训练，使模型在医疗、金融等领域生成连贯响应。
- 提出统一自适应动态网络构想，推动算法向智能演化发展。
本文探讨学习算法在各类应用中的重要性，涵盖模式与特征识别的训练方法，简要回顾人工智能（AI）、机器学习（ML）、深度学习（DL）及混合模型的核心概念。讨论了监督学习、无监督学习和强化学习等重要子集，适用于预测、分类与分割等任务。卷积神经网络（CNN）被用于图像与视频处理等场景，并分析其架构及与机器学习算法结合构建混合模型的方法。论文还探讨了学习算法对噪声的脆弱性导致误分类的问题。进一步研究将学习算法与大型语言模型（LLM）集成，通过海量数据学习关键模式，在医疗、营销、金融等多个领域生成连贯响应。最后展望下一代学习算法，提出构建统一的自适应动态网络以执行重要任务。整体提供学习算法当前状态、应用及未来方向的简明概述。
原文摘要 · Abstract (English)
In this paper, we discuss learning algorithms and their importance in different types of applications which includes training to identify important patterns and features in a straightforward, easy-to-understand manner. We will review the main concepts of artificial intelligence (AI), machine learning (ML), deep learning (DL), and hybrid models. Some important subsets of Machine Learning algorithms such as supervised, unsupervised, and reinforcement learning are also discussed in this paper. These techniques can be used for some important tasks like prediction, classification, and segmentation. Convolutional Neural Networks (CNNs) are used for image and video processing and many more applications. We dive into the architecture of CNNs and how to integrate CNNs with ML algorithms to build hybrid models. This paper explores the vulnerability of learning algorithms to noise, leading to misclassification. We further discuss the integration of learning algorithms with Large Language Models (LLM) to generate coherent responses applicable to many domains such as healthcare, marketing, and finance by learning important patterns from large volumes of data. Furthermore, we discuss the next generation of learning algorithms and how we may have an unified Adaptive and Dynamic Network to perform important tasks. Overall, this article provides brief overview of learning algorithms, exploring their current state, applications and future direction.
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