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Instructor
Halima Ramdani, Instructor - Recurrent and Recursive Networks

Halima Ramdani

Has a Master's Degree and pursuing her Ph.D. in Time Series Forecasting and Natural Language Processing. Her expertise spans on Machine Learning, AI, and Deep Learning.

Instructor: Halima Ramdani

Learn how to implement Recurrent Neural Networks!

  • Gain the knowledge and skills to effectively choose the right recurrent neural network model to solve real-world problems.
  • Implement a simple recurrent neural network in python.
  • Instructor has a Masters Degree and pursuing a PhD in Time Series Forecasting & NLP.

Duration: 2h 25m

Course Description

This course is designed to offer the audience an introduction to recurrent neural network, why and when use recurrent neural network, what are the variants of recurrent neural network, use cases, long-short term memory, deep recurrent neural network, recursive neural network, echo state network, implementation of sentiment analysis using RNN, and implementation of time series analysis using RNN.

What am I going to get from this course?

Understand:
  • What is a recurrent neural network.
  • Its different various such as recursive, echo state networks, LSTM and deep recurrent network.
  • Gain the knowledge and skills to effectively choose the right recurrent neural network model to solve real-world problems.
  • Implement a simple recurrent neural network in python.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • Familiarity with the Python programming language is required.
  • Students should be able to use Python 3.x and Jupyter Notebooks.

Who should take this course? Who should not?

  • College students who are interested in learning about recurrent neural networks in a simple and structured format should take this course.
  • Professionals who want to implement time forecasting NLP or computer vision models using Recurrent Neural Networks.

Curriculum

Module 1: Recurrent Neural Network

Lecture 1 Introduction to Neural Network
Lecture 2 What is a Recurrent Neural Network?
Lecture 3 How Does it Work?
Lecture 4 What are the Different Variants of it?
Quiz 1

Module 2: Long-short term memory

Lecture 5 Backpropagation through time
Lecture 6 Vanishing Gradients
Lecture 7 How to Prepare Data for Long-short Term Memory?
Lecture 8 Let's Implement a Long-short Term Memory
Quiz 2

Module 3: Deep recurrent neural network

Lecture 9 How Does it Work and What's its Structure?

Lecture 10 Variants of Depth
Lecture 11 Format Description of Deep Recurrent Neural Network

Quiz 3

Module 4: Recursive neural network

Lecture 12 Recurrent vs Recursive Neural Network
Lecture 13 Backpropagation Through Structure
Lecture 14 In Which Case Can we Use it?
Quiz 4

Module 5: Echo state networks

Lecture 15 How Does it Work and What's its Structure?

Lecture 16 How Do we Train it?
Lecture 17 When Use it?
Quiz 5 Echo State Networks

Module 6: Questions & Answers

Lecture 18 Q&A Module 1
Lecture 19 Q&A Module 2
Lecture 20 Q&A Module 3
Lecture 21 Q&A Module 4
Lecture 22 Q&A Module 5

Module 7: Final quiz

Lecture 23 Final Quiz Lecture

Module 8: Implement Recurrent neural network step by step in python

Lecture 24 Implementation in Python
350