Recurrent Neural Networks (RNN) Courses and Certifications

Recurrent Neural Networks (RNNs) for Language Modeling with Keras

Learn how to use RNNs to classify text sentiment, generate sentences, and translate text between languages.

• DataCamp
• 4 hours
• On-Demand
• Free Trial Available

RNNs and Transformers

This course covers multiple RNN architectures and discusses design patterns for those models. You'll also learn about transformer architectures.

• Udacity
• 3 hours
• On-Demand
• Paid Course

Packt

Advanced RNN Concepts and Projects

Master advanced RNN concepts, implement GRUs and LSTMs, and apply them to real-world projects like text generation and stock price prediction using TensorFlow.

• Coursera
• 6 hours 28 minutes
• On-Demand
• Paid Course

Recurrent Neural Networks

143 ratings at LinkedIn Learning

Learn the basics of recurrent neural networks to get up and running with RNN quickly.

• LinkedIn Learning
• 1 hour 7 minutes
• On-Demand
• Free Trial Available

Serrano.Academy

A Friendly Introduction to Recurrent Neural Networks

2 reviews

Explore how computers predict and generate sequences using Recurrent Neural Networks. Learn about vectors, neural networks, and practical applications in cooking, weather, and more.

• YouTube
• 23 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

MIT: Recurrent Neural Networks

2 reviews

Explore deep sequence modeling with recurrent neural networks, including LSTMs and attention mechanisms. Learn to tackle challenges like vanishing gradients and long-term dependencies in sequence tasks.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Brandon Rohrer

Recurrent Neural Networks and Long Short-Term Memory

1 review

Explore recurrent neural networks and LSTM, their applications in sequence prediction, and practical uses in machine learning and AI.

• YouTube
• 26 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

Recurrent Neural Networks and Transformers

Comprehensive exploration of recurrent neural networks, LSTMs, and attention mechanisms in deep learning, covering theory, implementation, and applications for sequence modeling tasks.

• YouTube
• 58 minutes
• On-Demand
• Free Video

Alexander Amini;  MIT

MIT 6.S191 - Recurrent Neural Networks

Comprehensive lecture on Recurrent Neural Networks, covering theory, implementation, and applications. Explores sequence modeling, LSTM, attention mechanisms, and practical examples in deep learning.

• YouTube
• 1 hour 1 minute
• On-Demand
• Free Video

Alexander Amini;  MIT

MIT 6.S191 - Recurrent Neural Networks

Explore recurrent neural networks, LSTMs, and attention mechanisms for sequence modeling. Learn backpropagation through time, gradient issues, and practical applications in deep learning.

• YouTube
• 45 minutes
• On-Demand
• Free Video

Yannic Kilcher

Transformers Are RNNs- Fast Autoregressive Transformers With Linear Attention

Explore a novel approach to transformers using linear attention, reducing computational complexity and revealing connections to RNNs. Gain insights into faster, more efficient deep learning models for long sequences.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Pixel Recurrent Neural Networks

Explore cutting-edge image generation techniques, focusing on Pixel Recurrent Neural Networks. Learn about model architectures, RNN concepts, and LSTM equations for advanced AI applications.

• YouTube
• 30 minutes
• On-Demand
• Free Video

Jay Alammar

The Unreasonable Effectiveness of RNNs - Article and Visualization Commentary

Explore the groundbreaking potential of RNNs in NLP through an insightful commentary on Karpathy's influential article, covering character-level models, visualizations, and their impact on modern AI.

• YouTube
• 29 minutes
• On-Demand
• Free Video

Yannic Kilcher

RWKV- Reinventing RNNs for the Transformer Era

Explore RWKV, a novel architecture combining Transformer and RNN strengths. Learn about its linear attention mechanism, scalability, and performance in NLP tasks.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Video

Advanced Computer Vision and Deep Learning

1 review

Learn to apply deep learning architectures to computer vision tasks. Discover how to combine CNN and RNN networks to build an automatic image captioning application.

• Udacity
• 9 hours
• On-Demand
• Paid Course