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Recurrent Neural Networks and Long Short-Term Memory

Brandon Rohrer via YouTube

Overview

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Explore the fundamentals of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) in this 26-minute video lecture. Delve into practical applications, starting with a dinner prediction scenario, and progress through key concepts including vectors and LSTM examples. Gain insights into the practical uses of these neural network architectures and access valuable resources for further learning.

Syllabus

Introduction
Whats for dinner
Predictions on dinner
Vectors
LSTM Example
Practical Applications
Resources

Taught by

Brandon Rohrer

Reviews

5.0 rating, based on 1 Class Central review

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  • This course is an outstanding introduction to RNNs and LSTMs! Brandon Rohrer’s teaching style is clear, engaging, and highly effective—he breaks down complex concepts into intuitive explanations without oversimplifying. The practical examples and hands-on exercises make it easy to grasp how these models work and why they’re essential for sequence-based tasks. The pacing is perfect, balancing theory with real-world applications, and the visuals/diagrams enhance understanding. Whether you're a beginner or looking to solidify your knowledge, this course delivers immense value. Brandon’s enthusiasm is contagious, making learning both enjoyable and rewarding. Highly recommended for anyone diving into deep learning and sequential data!

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