courseraАнализ данных

Learning Deep Learning: Generative AI and Large Language Models

Курс от Pearson

Средний≈ 6.5 чАнглийский

О курсе

This course covers key deep learning architectures such as BERT and GPT, focusing on their use in applications like chatbots and prompt tuning. You will learn how to build models that combine text and images, and generate text from visual data. The course also addresses multitask learning and computer vision tasks, including object detection and segmentation, using networks like R-CNN, U-Net, and Mask R-CNN. Topics include ethical considerations in AI and practical advice for tuning and deploying models. Through hands-on projects in TensorFlow and PyTorch, you will develop the skills needed to build, optimize, and apply deep learning solutions in real-world situations.

Навыки, которые вы освоите

Tensorflow Multimodal Prompts Large Language Modeling PyTorch (Machine Learning Library)Computer Vision Responsible AI Model Training Generative AI Model Optimization Image Analysis Prompt Engineering LLM Application Artificial Intelligence Generative Model Architectures Convolutional Neural Networks Model Deployment Fine-tuning Deep Learning

Программа курса

1 модулей · 38 учебных материалов

01Learning Deep Learning: Unit 338 материалов

Large Language Models

TopicsВидео Overview of BERTВидео Overview of GPTВидео From GPT to GPT4Видео Handling Chat HistoryВидео

Учитесь у экспертов

Pearson

Преподаватель курса

Magnus Ekman

Director of Architecture

Learning Deep Learning: Generative AI and Large Language Models

О курсе

Навыки, которые вы освоите

Программа курса

Large Language Models

Учитесь у экспертов

Pearson

Magnus Ekman

Multi-Modal Networks and Image Captioning

Multi-Task Learning and Computer Vision Beyond Classification

Applying Deep Learning

Learning Deep Learning: Summary