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Book Overview & Buying
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Table Of Contents
Deep Learning: Recurrent Neural Networks with Python
By :
Deep Learning: Recurrent Neural Networks with Python
By:
Overview of this book
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
Table of Contents (12 chapters)
Introduction
Applications of RNN
Deep Neural Network (DNN) Overview
RNN Architecture
Gradient Descent in RNN
RNN Implementation
Sentiment Classification Using RNN
Vanishing Gradients in RNN
Project 1: Book Writer
Project 2: Stock Price Prediction
Further Reading and Resources
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