**Deep Learning: Recurrent Neural Networks in Python**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 7 Hours | 1.37 GB

eLearning | Skill level: All Levels

GRU, LSTM, + more modern deep learning, machine learning, and data science for sequences

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences – but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not – and as a result, they are more expressive, and more powerful than anything we’ve seen on tasks that we haven’t made progress on in decades.

So what’s going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models?

In the first section of the course we are going to add the concept of time to our neural networks.

I’ll introduce you to the Simple Recurrent Unit, also known as the Elman unit.

We are going to revisit the XOR problem, but we’re going to extend it so that it becomes the parity problem – you’ll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

In the next section of the course, we are going to revisit one of the most popular applications of recurrent neural networks – language modeling.

You saw when we studied Markov Models that we could do things like generate poetry and it didn’t look too bad. We could even discriminate between 2 different poets just from the sequence of parts-of-speech tags they used.

In this course, we are going to extend our language model so that it no longer makes the Markov assumption.

Another popular application of neural networks for language is word vectors or word embeddings. The most common technique for this is called Word2Vec, but I’ll show you how recurrent neural networks can also be used for creating word vectors.

In the section after, we’ll look at the very popular LSTM, or long short-term memory unit, and the more modern and efficient GRU, or gated recurrent unit, which has been proven to yield comparable performance.

We’ll apply these to some more practical problems, such as learning a language model from Wikipedia data and visualizing the word embeddings we get as a result.

All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.

This course focuses on “how to build and understand”, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

All the code for this course can be downloaded from my github: /lazyprogrammer/machine_learning_examples

In the directory: rnn_class

Make sure you always “git pull” so you have the latest version!

What you’ll learn

- Understand the simple recurrent unit (Elman unit)
- Understand the GRU (gated recurrent unit)
- Understand the LSTM (long short-term memory unit)
- Write various recurrent networks in Theano
- Understand backpropagation through time
- Understand how to mitigate the vanishing gradient problem
- Solve the XOR and parity problems using a recurrent neural network
- Use recurrent neural networks for language modeling
- Use RNNs for generating text, like poetry
- Visualize word embeddings and look for patterns in word vector representations

**+ Table of Contents**

**Introduction and Outline**

1 Outline of this Course

2 Review of Important Deep Learning Concepts

3 Where to get the Code and Data

4 How to Succeed in this Course

**The Simple Recurrent Unit**

5 Architecture of a Recurrent Unit

6 Prediction and Relationship to Markov Models

7 Unfolding a Recurrent Network

8 Backpropagation Through Time (BPTT)

9 The Parity Problem – XOR on Steroids

10 The Parity Problem in Code using a Feedforward ANN

11 Theano Scan Tutorial

12 The Parity Problem in Code using a Recurrent Neural Network

13 On Adding Complexity

**Recurrent Neural Networks for NLP**

14 Word Embeddings and Recurrent Neural Networks

15 Word Analogies with Word Embeddings

16 Representing a sequence of words as a sequence of word embeddings

17 Generating Poetry

18 Generating Poetry in Code (part 1)

19 Generating Poetry in Code (part 2)

20 Classifying Poetry

21 Classifying Poetry in Code

**Advanced RNN Units**

22 Rated RNN Unit

23 Learning from Wikipedia Data in Code (part 2)

24 Visualizing the Word Embeddings

25 RRNN in Code – Revisiting Poetry Generation

26 Gated Recurrent Unit (GRU)

27 GRU in Code

28 Long Short-Term Memory (LSTM)

29 LSTM in Code

30 Learning from Wikipedia Data

31 Alternative to Wikipedia Data Brown Corpus

32 Learning from Wikipedia Data in Code (part 1)

**Batch Training**

33 Batch Training for Simple RNN

**TensorFlow**

34 Simple RNN in TensorFlow

**Basics Review**

35 (Review) Theano Basics

36 (Review) Theano Neural Network in Code

37 (Review) Tensorflow Basics

38 (Review) Tensorflow Neural Network in Code

**Appendix**

39 What is the Appendix

40 BONUS Where to get Udemy coupons and FREE deep learning material

41 Python 2 vs Python 3

42 Is Theano Dead

43 What order should I take your courses in (part 1)

44 What order should I take your courses in (part 2)

45 How to install wp2txt or WikiExtractor.py

46 Windows-Focused Environment Setup 2018

47 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

48 How to Code by Yourself (part 1)

49 How to Code by Yourself (part 2)

50 How to Succeed in this Course (Long Version)

51 Is this for Beginners or Experts Academic or Practical Fast or slow-paced

52 Proof that using Jupyter Notebook is the same as not using it

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