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所在平台: Udemy |
课程主页: https://www.udemy.com/course/mastering-ai-with-transformers-and-llms/
课程评论：没有评论
课程名称：AI与大型语言模型和变换器：从理论到部署-2025 课程概述： “AI与大型语言模型和变换器（A-Z）”不仅仅是一门课程，而是一个变革性的体验。它为学习者提供了在不断变化的人工智能领域中导航和领导所需的专业知识、实践技能和创新思维。通过项目驱动的实践学习和实用应用，本课程将帮助学习者掌握从模型训练、微调到部署的全方位技能。 课程亮点： - 实践导向，真实案例应用。 - 从行业专业人士那里学习深入的AI知识。 - 增强解决问题的能力，通过真实案例研究理解AI的挑战与解决方案。 课程内容： 1. **介绍（了解变换器）**： - 理解变换器管道模块及其内部工作原理。 - 高层次理解变换器架构及语言模型的重要性。 2. **变换器架构**： - 输入嵌入与位置编码的基本概念。 - 深入了解编码器和解码器的功能。 - 探索不同类型的语言模型（如BERT、GPT）。 3. **文本分类**： - 实践经验：微调BERT以进行多类分类和情感分析。 4. **问答系统**： - 理解问答任务的直观理解。 - 建立基于亚马逊评论的问答系统并实施检索器-阅读器方法。 5. **文本生成**： - 探索不同的解码方法以及训练自己的生成预训练变换器（GPT）。 6. **文本摘要**： - 了解不同文本摘要模型（如GPT2、T5、BART）。 - 实践微调PEGASUS进行对话摘要。 7. **从零开始构建自己的变换器**： - 构建自定义标记器，准备数据，实施位置嵌入和变换器架构。 8. **在生产环境中部署变换器模型**： - 模型优化技术，如知识蒸馏和量化，了解如何使用ONNX优化模型，使用Fast API和Docker服务部署变换器模型。 课程结尾： 完成本课程后，学习者将对变换器的功能有深入的理解，能够将理论知识转化为实践技能，能够精确微调模型以满足特定需求，并在人工智能领域创建、训练及部署AI模型，产生显著影响。
AI with LLMs and Transformers (A-Z) isn't just a course; it's a transformative experience that arms learners with the expertise, practical skills, and innovation-driven mindset needed to navigate and lead in the ever-evolving landscape of Artificial Intelligence.Why Take This Course?Hands-on, project-based learning with real-world applicationsStep-by-step guidance on training, fine-tuning, and deploying modelsCovers both theory and practical implementationLearn from industry professionals with deep AI expertiseGain the skills to build and deploy custom AI solutionsUnderstand challenges and solutions in large-scale AI deploymentEnhance problem-solving skills through real-world AI case studiesWhat You'll Learn:Section 1: Introduction ( Understanding Transformers):Explore Transformer's Pipeline Module:Understand the step-by-step process of how data flows through a Transformer model, gaining insights into the model's internal workings.High-Level Understanding of Transformers Architecture:Grasp the overarching architecture of Transformers, including the key components that define their structure and functionality.What are Language Models:Gain an understanding of language models, their significance in natural language processing, and their role in the broader field of artificial intelligence.Section 2: Transformers ArchitectureInput Embedding:Learn the essential concept of transforming input data into a format suitable for processing within the Transformer model.Positional Encoding:Explore the method of adding positional information to input embeddings, a crucial step for the model to understand the sequential nature of data.The Encoder and The Decoder:Dive into the core components of the Transformer architecture, understanding the roles and functionalities of both the encoder and decoder.Autoencoding LM - BERT, Autoregressive LM - GPT, Sequence2Sequence LM - T5:Explore different types of language models, including their characteristics and use cases.Tokenization:Understand the process of breaking down text into tokens, a foundational step in natural language processing.Section 3: Text ClassificationFine-tuning BERT for Multi-Class Classification:Gain hands-on experience in adapting pre-trained models like BERT for multi-class classification tasks.Fine-tuning BERT for Sentiment Analysis:Learn how to fine-tune BERT specifically for sentiment analysis, a common and valuable application in NLP.Fine-tuning BERT for Sentence-Pairs:Understand the process of fine-tuning BERT for tasks involving pairs of sentences.Section 4: Question AnsweringQA Intuition:Develop an intuitive understanding of question-answering tasks and their applications.Build a QA System Based Amazon ReviewsImplement Retriever Reader ApproachFine-tuning transformers for question answering systemsTable QASection 5: Text GenerationGreedy Search Decoding, Beam Search Decoding, Sampling Methods:Explore different decoding methods for generating text using Transformer models.Train Your Own GPT:Acquire the skills to train your own Generative Pre-trained Transformer model for creative text generation.Section 6: Text SummarizationIntroduction to GPT2, T5, BART, PEGASUS:Understand the characteristics and applications of different text summarization models.Evaluation Metrics - Bleu Score, ROUGE:Learn the metrics used to evaluate the effectiveness of text summarization, including Bleu Score and ROUGE.Fine-Tuning PEGASUS for Dialogue Summarization:Gain hands-on experience in fine-tuning PEGASUS specifically for dialogue summarization.Section 7: Build Your Own Transformer From ScratchBuild Custom Tokenizer:Construct a custom tokenizer, an essential component for processing input data in your own Transformer.Getting Your Data Ready:Understand the importance of data preparation and how to format your dataset for training a custom Transformer.Implement Positional Embedding, Implement Transformer Architecture:Gain practical skills in implementing positional embedding and constructing the entire Transformer architecture from scratch.Section 8: Deploy the Transformers Model in the Production EnvironmentModel Optimization with Knowledge Distillation and Quantization:Explore techniques for optimizing Transformer models, including knowledge distillation and quantization.Model Optimization with ONNX and the ONNX Runtime:Learn how to optimize models using the ONNX format and runtime.Serving Transformers with Fast API, Dockerizing Your Transformers APIs:Acquire the skills to deploy and serve Transformer models in production environments using Fast API and Docker.Becoming a Transformer Maestro:By the end of the course:Learners will possess an intimate understanding of how Transformers function, making them true Transformer maestros capable of navigating the ever-evolving landscape of AI innovation.Learners will be able to translate theoretical knowledge into hands-on skillsUnderstand how to fine-tune models for specific needs using your own datasets.By the end of this course, you will have the expertise to create, train, and deploy AI models, making a significant impact in the field of artificial intelligence.
