courseraАнализ данных

Introduction to RNN and DNN

Курс от Packt

Начальный≈ 6.9 чАнглийский

О курсе

Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies. You’ll start with the basics of RNNs and their applications, followed by an examination of DNNs, including their architecture and implementation using PyTorch. You will master building and deploying sophisticated AI models, develop RNN models for tasks like speech recognition and machine translation, understand and implement DNN architectures, and utilize PyTorch for model building and optimization. By the end, you'll have a robust knowledge of RNNs and DNNs and the confidence to apply these techniques in real-world scenarios. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming (preferably Python) and statistics knowledge, this course combines theory with practical application through video lectures, hands-on exercises, and real-world examples.

Навыки, которые вы освоите

Model Training Deep Learning Model Optimization PyTorch (Machine Learning Library)Artificial Neural Networks Artificial Intelligence Data Science Network Architecture Artificial Intelligence and Machine Learning (AI/ML)Recurrent Neural Networks (RNNs)Model Deployment Applied Machine Learning Application Deployment Machine Learning

Программа курса

3 модулей · 59 учебных материалов

01Introduction4 материалов

Introduction

Introduction to the SpecializationВидео Introduction to the Course 'Introduction to RNN and DNN'Чтение Full Specialization ResourcesЧтение Focus of the CourseВидео

02Applications of RNN

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Packt - Course Instructors

Преподаватель курса