English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 07m | 159 MB

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.

## Table of Contents

**Introduction**

1 Getting started with RNNs

2 Scope and prerequisites for the course

3 Setting up exercise files

**Introduction to RNNs**

4 A review of deep learning

5 Why sequence models

6 A recurrent neural network

7 Types of RNNs

8 Applications of RNNs

**RNN Concepts**

9 Training RNN models

10 Forward propagation with RNN

11 Computing RNN loss

12 Backward propagation with RNN

13 Predictions with RNN

**An RNN Example**

14 A simple RNN example Predicting stock prices

15 Data preprocessing for RNN

16 Preparing time series data with lookback

17 Creating an RNN model

18 Testing and predictions with RNN

**RNN Architectures**

19 The vanishing gradient problem

20 The gated recurrent unit

21 Long short-term memory

22 Bidirectional RNNs

**An LSTM Example**

23 Forecasting service loads with LSTM

24 Time series patterns

25 Preparing time series data for LSTM

26 Creating an LSTM model

27 Testing the LSTM model

28 Forecasting service loads Predictions

**Word Embeddings**

29 Text based models Challenges

30 Intro to word embeddings

31 Pretrained word embeddings

32 Text preprocessing for RNN

33 Creating an embedding matrix

**Spam Detection with Word Embeddings**

34 Spam detection example for embeddings

35 Preparing spam data for training

36 Building the embedding matrix

37 Creating a spam classification model

38 Predicting spam with LSTM and word embeddings

**Conclusion**

39 Next steps

Resolve the captcha to access the links!