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Deep Learning: Recurrent Neural Networks with Python [Video]
Deep Learning: Recurrent Neural Networks with Python [Video]

Deep Learning: Recurrent Neural Networks with Python: Master, train, and build recurrent neural networks with Python [Video]

By AI Sciences
$44.99
Video Feb 2021 15 hours 35 minutes 1st Edition
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Key benefits

  • Understand and apply fundamentals of recurrent neural networks
  • Implement RNNs and related architectures on real-world datasets
  • Train RNNs for real-world applications—automatic book writer and stock price prediction

Description

With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority. This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow. The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application. By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications. The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks

What you will learn

Gain an overview of deep neural networks Understand the fundamentals of RNN architectures Train real-world datasets using different RNN architectures Implement RNNs, LSTM, and GRUs through hands-on exercises Create and compile RNN models in TensorFlow Perform text classification using RNNs and TensorFlow

Product Details

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Publication date : Feb 26, 2021
Length 15 hours 35 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781801079167
Category :
Concepts :

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
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Product Details


Publication date : Feb 26, 2021
Length 15 hours 35 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781801079167
Category :
Concepts :

Table of Contents

12 Chapters
1. Introduction Chevron down icon Chevron up icon
2. Applications of RNN Chevron down icon Chevron up icon
3. Deep Neural Network (DNN) Overview Chevron down icon Chevron up icon
4. RNN Architecture Chevron down icon Chevron up icon
5. Gradient Descent in RNN Chevron down icon Chevron up icon
6. RNN Implementation Chevron down icon Chevron up icon
7. Sentiment Classification Using RNN Chevron down icon Chevron up icon
8. Vanishing Gradients in RNN Chevron down icon Chevron up icon
9. TensorFlow Chevron down icon Chevron up icon
10. Project 1: Book Writer Chevron down icon Chevron up icon
11. Project 2: Stock Price Prediction Chevron down icon Chevron up icon
12. Further Reading and Resources Chevron down icon Chevron up icon

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