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30-Day Money-Back Guarantee
Development Data Science Python

Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence
Rating: 4.6 out of 54.6 (3,040 ratings)
23,924 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Italian [Auto], 
30-Day Money-Back Guarantee

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
Curated for the Udemy for Business collection

Course content

13 sections • 70 lectures • 11h 32m total length

  • Preview03:18
  • Where to get the Code
    08:26
  • How to Succeed in this Course
    05:51

  • 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

  • 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
  • Preview09:58
  • How does a model "learn"?
    10:53
  • Making Predictions
    06:45
  • Saving and Loading a Model
    04:27
  • Suggestion Box
    03:03

  • 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

  • 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

  • Embeddings
    13:12
  • Code Preparation (NLP)
    13:17
  • Text Preprocessing
    05:30
  • Text Classification with LSTMs
    08:19

  • Mean Squared Error
    09:11
  • Binary Cross Entropy
    05:58
  • Categorical Cross Entropy
    08:06

  • Gradient Descent
    07:52
  • Stochastic Gradient Descent
    04:36
  • Momentum
    06:10
  • Variable and Adaptive Learning Rates
    11:45
  • Adam
    11:18

  • Colab Notebooks
    00:09

  • Windows-Focused Environment Setup 2018
    20:20
  • How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
    17:32

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!


"If you can't implement it, you don't understand it"

  • Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

  • My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

  • Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

  • After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...


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 "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ 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

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,181 Reviews
  • 422,558 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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