Recurrent Neural Networks

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Kumaran Ponnambalam

1:07:06

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01 - Introduction

01 - Getting started with RNNs.mp4

00:58

02 - Scope and prerequisites for the course.mp4

02:04

03 - Setting up exercise files.mp4

02:35

02 - 1. Introduction to RNNs

01 - A review of deep learning.mp4

02:15

02 - Why sequence models.mp4

02:04

03 - A recurrent neural network.mp4

02:33

04 - Types of RNNs.mp4

02:11

05 - Applications of RNNs.mp4

01:35

03 - 2. RNN Concepts

01 - Training RNN models.mp4

01:25

02 - Forward propagation with RNN.mp4

02:16

03 - Computing RNN loss.mp4

01:06

04 - Backward propagation with RNN.mp4

01:28

05 - Predictions with RNN.mp4

00:51

04 - 3. An RNN Example

01 - A simple RNN example Predicting stock prices.mp4

01:16

02 - Data preprocessing for RNN.mp4

01:03

03 - Preparing time series data with lookback.mp4

02:25

04 - Creating an RNN model.mp4

01:37

05 - Testing and predictions with RNN.mp4

01:38

05 - 4. RNN Architectures

01 - The vanishing gradient problem.mp4

02:04

02 - The gated recurrent unit.mp4

02:58

03 - Long short-term memory.mp4

01:50

04 - Bidirectional RNNs.mp4

02:57

06 - 5. An LSTM Example

01 - Forecasting service loads with LSTM.mp4

01:26

02 - Time series patterns.mp4

01:56

03 - Preparing time series data for LSTM.mp4

01:14

04 - Creating an LSTM model.mp4

00:41

05 - Testing the LSTM model.mp4

01:30

06 - Forecasting service loads Predictions.mp4

02:11

07 - 6. Word Embeddings

01 - Text based models Challenges.mp4

01:41

02 - Intro to word embeddings.mp4

03:57

03 - Pretrained word embeddings.mp4

01:50

04 - Text preprocessing for RNN.mp4

01:39

05 - Creating an embedding matrix.mp4

01:13

08 - 7. Spam Detection with Word Embeddings

01 - Spam detection example for embeddings.mp4

01:03

02 - Preparing spam data for training.mp4

01:29

03 - Building the embedding matrix.mp4

01:40

04 - Creating a spam classification model.mp4

00:57

05 - Predicting spam with LSTM and word embeddings.mp4

00:50

09 - Conclusion

01 - Next steps.mp4

00:40

Description

Get started with recurrent neural network (RNN) concepts in a simplified way and build simple applications with RNNs and Keras. RNN is a fast-growing domain within the AI world. Popular groundbreaking applications like language translation, speech synthesis, question answering, and text generation use RNNs as their base technology. Studying this technology, however, has several challenges. Most learning resources are math heavy and are difficult to navigate without good math skills. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, Kumaran Ponnambalam provides a simplified path to studying the basics of recurrent neural networks, allowing you to become productive quickly. Kumaran starts with a simplified introduction of RNN before walking through the process of building a model. He then covers the popular building blocks of RNN with GRUs, LSTMs, word embeddings, and transformers.

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Neural Networks

Kumaran Ponnambalam

Instructor's Courses

A seasoned veteran in everything data, with a reputation for delivering high performance database and SaaS applications and currently specializing in leading Big Data Science and Engineering efforts

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