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GRU

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May 1, 2024 5 minute read

**Gated Recurrent Unit (GRU)** is a type of recurrent neural network (RNN) that is used in natural language processing, speech recognition, and other sequential data applications. GRU was developed to address the vanishing gradient problem, which can occur in traditional RNNs when the sequence to be processed is very long. The GRU is a member of the family of Gated Recurrent Units (GRUs). GRUs are designed to avoid the vanishing gradient problem, and are well-suited to processing sequential data. These models usually achieve state-of-the-art results in many applications like natural language processing and speech recognition.

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Path to GRU

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We've curated three courses to help you on your path to GRU. Use these to develop your skills, build background knowledge, and put what you learn to practice.

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Processamento Neural de Linguagem Natural em Português I

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Sequence Models

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Fundamentals of CNNs and RNNs

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in GRU.

Deep Learning with R

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Comprehensive guide to deep learning and covers various types of neural networks, including GRUs. It provides detailed explanations and practical examples, making it suitable for both beginners and experienced practitioners.

Deep Learning

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Foundational text on deep learning and includes a chapter on GRUs. It provides a theoretical overview and practical guidance on implementing GRUs for various tasks.

Deep Learning (Adaptive Computation and Machine...

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Deep Learning (Adaptive Computation and Machine...

Kindle Edition

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Relevant careers

Machine Learning Engineer

Data Scientist

Natural Language Processing Engineer

Software Engineer

Data Analyst

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