Deep Learning: Recurrent Neural Networks in Python
4.5 (2,710 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
21,506 students enrolled

Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence
4.5 (2,710 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
21,506 students enrolled
Last updated 8/2020
English
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Current price: $83.99 Original price: $119.99 Discount: 30% off
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This course includes
  • 10.5 hours on-demand video
  • 1 article
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Apply RNNs to Time Series Forecasting (tackle the ubiquitous "Stock Prediction" problem)
  • Apply RNNs to Natural Language Processing (NLP) and Text Classification (Spam Detection)
  • Apply RNNs to Image Classification
  • Understand the simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit)
  • Write various recurrent networks in Tensorflow 2
  • Understand how to mitigate the vanishing gradient problem
Course content
Expand all 64 lectures 10:39:37
+ Google Colab
3 lectures 33:07
Intro to Google Colab, how to use a GPU or TPU for free
12:32
Uploading your own data to Google Colab
11:41
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
08:54
+ Machine Learning and Neurons
12 lectures 01:39:18
Review Section Introduction
02:37
What is Machine Learning?
14:26
Code Preparation (Classification Theory)
15:59
Beginner's Code Preamble
04:38
Classification Notebook
08:40
Code Preparation (Regression Theory)
07:18
Regression Notebook
10:34
How does a model "learn"?
10:53
Making Predictions
06:45
Saving and Loading a Model
04:27
Suggestion Box
03:03
+ Feedforward Artificial Neural Networks
9 lectures 01:36:21
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
08:41
How to Represent Images
12:36
Code Preparation (ANN)
12:42
ANN for Image Classification
08:36
ANN for Regression
11:05
+ Recurrent Neural Networks, Time Series, and Sequence Data
18 lectures 03:12:29
Sequence Data
18:27
Forecasting
10:35
Autoregressive Linear Model for Time Series Prediction
12:01
Proof that the Linear Model Works
04:12
Recurrent Neural Networks
21:34
RNN Code Preparation
05:50
RNN for Time Series Prediction
11:11
Paying Attention to Shapes
08:27
GRU and LSTM (pt 1)
16:09
GRU and LSTM (pt 2)
11:36
A More Challenging Sequence
09:19
Demo of the Long Distance Problem
19:26
RNN for Image Classification (Theory)
04:41
RNN for Image Classification (Code)
04:00
Stock Return Predictions using LSTMs (pt 1)
12:03
Stock Return Predictions using LSTMs (pt 2)
05:45
Stock Return Predictions using LSTMs (pt 3)
11:59
Other Ways to Forecast
05:14
+ Natural Language Processing (NLP)
4 lectures 40:18
Embeddings
13:12
Code Preparation (NLP)
13:17
Text Preprocessing
05:30
Text Classification with LSTMs
08:19
+ Extras
1 lecture 00:09
Colab Notebooks
00:09
+ Setting Up Your Environment
2 lectures 37:52
Windows-Focused Environment Setup 2018
20:20
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
17:32
+ Extra Help With Python Coding for Beginners
6 lectures 54:48
How to install wp2txt or WikiExtractor.py
02:21
How to Code by Yourself (part 1)
15:54
How to Code by Yourself (part 2)
09:23
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
+ Effective Learning Strategies for Machine Learning
4 lectures 59:53
How to Succeed in this Course (Long Version)
10:24
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:04
What order should I take your courses in? (part 1)
11:18
What order should I take your courses in? (part 2)
16:07
Requirements
  • Basic math (taking derivatives, matrix arithmetic, probability) is helpful
  • Python, Numpy, Matplotlib
Description

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***

Learn about one of the most powerful Deep Learning architectures yet!

The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.

This includes time series analysis, forecasting and natural language processing (NLP).

Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.

This course will teach you:

  • The basics of machine learning and neurons (just a review to get you warmed up!)

  • Neural networks for classification and regression (just a review to get you warmed up!)

  • How to model sequence data

  • How to model time series data

  • How to model text data for NLP (including preprocessing steps for text)

  • How to build an RNN using Tensorflow 2

  • How to use a GRU and LSTM in Tensorflow 2

  • How to do time series forecasting with Tensorflow 2

  • How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it's not what you think!)

  • How to use Embeddings in Tensorflow 2 for NLP

  • How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)

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 Tensorflow. 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.

See you in class!



Suggested Prerequisites:

  • matrix addition, multiplication

  • basic probability (conditional and joint distributions)

  • Python coding: if/else, loops, lists, dicts, sets

  • Numpy coding: matrix and vector operations, loading a CSV file


WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)


Who this course is for:
  • Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP
  • Software Engineers and Data Scientists who want to level up their career