English | Size: 1.2 GB
Genre: eLearning
Hugging Face – 15% theory 85% hands-on Lab
What you’ll learn
Grasp the core concepts of the Hugging Face ecosystem
Learn how to prepare datasets and fine-tune pretrained models for specific tasks,
Complete a case study to manage a project from conception to completion, utilizing Hugging Face resources to build
Gain the skills to implement real-world applications using Hugging Face models and pipelines, including creating and deploying NLP
Welcome to “Hugging Face with Fine-Tune LLM,” a comprehensive course designed to empower you with the skills and knowledge needed to harness the power of Hugging Face’s cutting-edge NLP tools and techniques. This course will take you through the essentials of working with large language models (LLMs) and guide you in fine-tuning them for various natural language processing tasks.
What You’ll Learn:
- Fundamentals of Hugging Face:
- Gain a solid understanding of the Hugging Face ecosystem and its significance in the field of NLP.
- Learn to navigate and utilize the Hugging Face Transformers library effectively.
- Working with Pre-trained Models:
- Explore the architecture and applications of popular models like BERT, GPT, and T5.
- Learn to load and deploy pre-trained models for tasks such as text classification, named entity recognition, and text generation.
- Fine-Tuning Large Language Models:
- Understand the process of preparing datasets for fine-tuning.
- Master the techniques for fine-tuning models on custom datasets to achieve high accuracy and performance.
- Model Evaluation and Optimization:
- Discover methods to evaluate the performance of your models using appropriate metrics.
- Learn to interpret results and optimize models for better efficiency and accuracy.
- Advanced Techniques and Deployment:
- Delve into advanced techniques like model distillation, quantization, and pruning.
- Gain insights into deploying models using Hugging Face’s Inference API and other strategies to bring your NLP solutions to production.
- Hands-On Projects and Case Studies:
- Engage in real-world projects that reinforce your learning and provide practical experience.
- Analyze case studies to understand the application of best practices in various NLP scenarios.
Who Should Enroll:
This course is ideal for:
- Data Scientists and Machine Learning Engineers seeking to deepen their expertise in NLP.
- Software Developers interested in integrating NLP features into their applications.
- Researchers and Academicians aiming to apply advanced NLP techniques in their work.
Who this course is for:
- Data Scientists and Machine Learning Engineers
- Business Analysts and Data Analysts
- Product Managers and Technical Leaders
- Students and Graduates
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