Simple Recurrent Neural Network with Keras from Coursera

In this hands-on project, you will use Keras with TensorFlow as its backend to create a recurrent neural network model and train it to learn to perform addition of simple equations given in string format. You will learn to create synthetic data for this problem as well. By the end of this 2-hour long project, you will have created, trained, and evaluated a sequence to sequence RNN model in Keras. Computers are already pretty good at math, so this may seem like a trivial problem, but it’s not! We will give the model string data rather than numeric data to work with. This means that the model needs to infer the meaning of various characters from a sequence of text input and then learn addition from the given data.

This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Tensorflow pre-installed.

Please note that you will need some experience in Python programming, and a theoretical understanding of Neural Networks to be able to finish this project successfully.

Notes:

- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.

- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

What's inside

Syllabus

Simple Recurrent Neural Network with Keras

Welcome to this project-based course on creating and training a simple recurrent neural network using Keras and TensorFlow. In this project, you will use Keras with TensorFlow as its backend to create a recurrent neural network model and train it to learn to perform addition of simple equations given in string format. You will learn to create synthetic data for this problem as well. By the end of this 2-hour long project, you will have created, trained, and evaluated a sequence to sequence RNN model in Keras. Computers are already pretty good at math, so this may seem like a trivial problem, but it’s not! The interesting part here is that we will give the model string data and not numeric data to work with. This means that the model needs to infer the meaning of various characters from a sequence of text input and then learn addition from the given data!

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

This course is designed for individuals with some experience in Python programming and a theoretical understanding of Neural Networks who want to learn how to create and train a simple recurrent neural network model using Keras and TensorFlow

Reviews summary

Well-received intro to rnns with python

Learners say the Simple Recurrent Neural Network with Keras course is a well-received intro to RNNs that is organized and well-guided, though some learners wish for detailed model architecture and think the course could be more rigorous. Learners liked the engaging application example despite finding the processing logic of the RNN to be vague and lacking in visual aids like figures or illustrations to support understanding. Despite a complaint about outdated code, many learners largely enjoyed their learning experience.

Application example is engaging

"The application example, despite being simple, is interesting from a didactic perspective."

Favorably reviewed by learners

"Excellent planning and guidance throughout"

"Excellent tool"

"Best Understanding of Recurrent Neural Network in simplest way."

Course lacks visual aids

"No figure was shown to illustrate the neural network."

Python code is outdated

"The Python codes in the notebook were created 5 years ago."

Processing logic of RNN is vague

"The processing logic of the RNN was explained vaguely."

"For example, it was not well explained why the model used must be formed by a combination of "encoder" & "decoder". It was also not clearly explained how we can check the number of inputs and outputs of each layer of the sequential model, to facilitate understanding of the model."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Simple Recurrent Neural Network with Keras with these activities:

Attend a Meetup on Artificial Intelligence

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Attending a Meetup on artificial intelligence will allow you to connect with other people who are interested in the field and learn about new developments.

Browse courses on Artificial Intelligence

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Find a Meetup on artificial intelligence in your area.
Register for the Meetup.
Attend the Meetup.

Create a List of Resources on Neural Networks

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Creating a list of resources on neural networks will help you stay up-to-date on the latest developments in the field and find additional materials to support your learning.

Browse courses on Neural Networks

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Search for online resources on neural networks.
Evaluate the quality of the resources.
Organize the resources into a list.

Follow a Tutorial on Keras

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Following a tutorial on Keras will help you learn the basics of the library and how to use it for building neural networks.

Browse courses on Keras

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Find a reputable tutorial on Keras.
Follow the instructions in the tutorial to create a simple neural network.
Test the neural network on a dataset.

Five other activities

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Show all eight activities

Practice Writing Equations for Addition

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Practice writing equations for addition to improve your understanding of the concept.

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Create a list of simple addition equations.
Write out each equation using the correct mathematical symbols.
Check your work by solving each equation.

Create a Diagram of a Simple Neural Network

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Creating a diagram of a simple neural network will help you visualize how it works and understand its structure.

Browse courses on Neural Networks

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Draw a circle to represent the input layer.
Draw a series of circles connected to the input layer to represent the hidden layer.
Draw another circle connected to the hidden layer to represent the output layer.
Label the input, hidden, and output layers.
Draw arrows connecting the circles to represent the flow of data.

Create a Python Script to Train a Simple RNN Model

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Creating a Python script to train a simple RNN model will help you apply your knowledge of Keras and RNNs to a practical problem.

Browse courses on Python

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Import the necessary libraries.
Load the dataset.
Create the RNN model.
Train the model.
Evaluate the model.

Review 'Deep Learning with Python'

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Reviewing 'Deep Learning with Python' will help you reinforce your understanding of the concepts covered in the course and learn more about deep learning in general.

View Deep Learning with Python, Second Edition on Amazon

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Read the book.
Take notes on the key concepts.
Complete the practice exercises.

Attend a Workshop on Machine Learning

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Attending a workshop on machine learning will provide you with an opportunity to learn from experts and ask questions about the field.

Browse courses on Machine Learning

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Find a reputable workshop on machine learning.
Register for the workshop.
Attend the workshop.

Career center

Learners who complete Simple Recurrent Neural Network with Keras will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists utilize computer science techniques to study large amounts of data in order to discover hidden patterns and trends. As a Data Scientist, you would use your knowledge of neural networks to develop innovative solutions for complex problems, such as predicting customer churn or identifying fraudulent transactions. This course would be particularly valuable for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are essential for many data science applications.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers design and develop machine learning models that can learn from data and make predictions. As a Machine Learning Engineer, you would use your knowledge of neural networks to build models that can solve a variety of problems, such as image recognition, natural language processing, and speech recognition. This course would be particularly useful for you as it would provide you with a hands-on experience in building and training recurrent neural networks.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. As a Software Engineer, you would use your knowledge of neural networks to develop innovative software solutions for a variety of industries. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in software development.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts collect, analyze, and interpret data to identify trends and patterns. As a Data Analyst, you would use your knowledge of neural networks to develop data-driven insights that can help businesses make better decisions. This course would be particularly valuable for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are essential for many data analysis applications.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. As a Quantitative Analyst, you would use your knowledge of neural networks to develop trading strategies and risk management models. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in quantitative finance.

See salaries and explore the career path for Quantitative Analyst

Financial Analyst

Financial Analysts use financial data to make investment recommendations. As a Financial Analyst, you would use your knowledge of neural networks to develop models that can predict stock prices and identify investment opportunities. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in financial analysis.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess risk. As an Actuary, you would use your knowledge of neural networks to develop models that can predict the likelihood of events such as accidents, illnesses, and deaths. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in actuarial science.

See salaries and explore the career path for Actuary

Statistician

Statisticians collect, analyze, and interpret data to draw conclusions about the world around us. As a Statistician, you would use your knowledge of neural networks to develop statistical models that can answer complex questions. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in statistics.

See salaries and explore the career path for Statistician

Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve complex problems in business and industry. As an Operations Research Analyst, you would use your knowledge of neural networks to develop models that can optimize processes and improve efficiency. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in operations research.

See salaries and explore the career path for Operations Research Analyst

Risk Manager

Risk Managers identify, assess, and mitigate risks. As a Risk Manager, you would use your knowledge of neural networks to develop models that can predict the likelihood and impact of risks. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in risk management.

See salaries and explore the career path for Risk Manager

Business Analyst

Business Analysts use data and analysis to help businesses make better decisions. As a Business Analyst, you would use your knowledge of neural networks to develop models that can identify trends and patterns in data. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in business analysis.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers develop and manage products. As a Product Manager, you would use your knowledge of neural networks to develop products that meet the needs of users. This course would be particularly helpful for you as it would provide you with a strong foundation in the fundamentals of recurrent neural networks, which are becoming increasingly popular in product development.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers develop and execute marketing campaigns. As a Marketing Manager, you would use your knowledge of neural networks to develop models that can predict the effectiveness of marketing campaigns. This course may be helpful for you as it would provide you with a basic understanding of neural networks, which are becoming increasingly popular in marketing.

See salaries and explore the career path for Marketing Manager

Sales Manager

Sales Managers develop and execute sales strategies. As a Sales Manager, you would use your knowledge of neural networks to develop models that can predict the likelihood of closing a sale. This course may be helpful for you as it would provide you with a basic understanding of neural networks, which are becoming increasingly popular in sales.

See salaries and explore the career path for Sales Manager

Customer Success Manager

Customer Success Managers help customers achieve success with a product or service. As a Customer Success Manager, you would use your knowledge of neural networks to develop models that can predict the likelihood of customer churn. This course may be helpful for you as it would provide you with a basic understanding of neural networks, which are becoming increasingly popular in customer success.

See salaries and explore the career path for Customer Success Manager