Build Multilayer Perceptron Models with Keras from Coursera

In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend. We will be working with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It's a very simple, widely used toy dataset for text classification. There are 46 different topics, some of which are more represented than others. But each topic has at least 10 examples in the training set. So in this project, you will build a MLP feed-forward neural network to classify Reuters newswires into 46 different mutually-exclusive topics.

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 Keras pre-installed.

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

Project: Build Multilayer Perceptron Models with Keras

In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend for multiclass classification. We will be working with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It's a very simple, widely used toy dataset for text classification. There are 46 different topics, some of which are more represented than others. But each topic has at least 10 examples in the training set. So in this project, you will build a MLP feed-forward neural network to classify Reuters newswires into 46 different mutually-exclusive topics.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Utilizes a hands-on approach, which improves comprehension and retention

Implements Keras, a commonly used deep learning library, providing industry-relevant skills

Utilizes TensorFlow as its backend, ensuring compatibility with widely adopted deep learning frameworks

Emphasizes multiclass classification, a fundamental concept in machine learning

Employs a toy dataset, potentially limiting its practical applications

Intended for learners with prior knowledge in machine learning and neural networks

Reviews summary

Introduction to keras

According to students, this course provides a nice and quick introduction to Multilayer Perceptrons using the Keras library. Beginners may especially enjoy this course. Learners say the course includes a simple, hands-on project although it could use more written instructions. The workload is manageable, although some students felt that the course was too easy and suggest that the difficulty level be increased.

Workload is manageable

"Professor taught course quite well and work load was bearable."

Good option for those starting out

"very helpful for beginner"

"Nice project for practice. For those who are beginner it is very good for them to do practice."

Simple hands-on project

"very easy to follow and practice this simple hands-on project !"

Difficulty level is too low

"Though it was soooooo easy course I would suggest Professor to increase the difficulty level by adding another week."

Limited written instructions

"Nice project, could be a bit better with more written instructions."

Quiz questions need improvement

"Good project, but the questions in the quiz need to be updated, clear and detailed explanatios in every step."

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 Build Multilayer Perceptron Models with Keras with these activities:

Review Python basics

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Ensure you have a solid foundation in Python before taking this course.

Browse courses on Python

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Review basic Python syntax
Complete a few simple Python exercises

Read 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition'

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Review the foundational principles of machine learning and deep learning to ensure you have a solid foundation before taking this course.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the first three chapters
Complete the exercises in the first three chapters

Follow a tutorial on building a text classification model with Keras

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Gain hands-on experience by following a guided tutorial on building a text classification model with Keras, similar to the project in this course.

Browse courses on Text Classification

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Find a suitable tutorial
Follow the tutorial step-by-step
Run the code and review the results

Five other activities

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Show all eight activities

Solve practice problems on multilayer perceptrons

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Deepen your understanding of how multilayer perceptrons work by solving a series of practice problems.

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Find practice problems or exercises on multilayer perceptrons
Attempt to solve the problems independently
Review your solutions against examples or ask for help if needed

Participate in a study group to discuss multilayer perceptrons and Keras

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Enhance your understanding by discussing concepts and working through problems with peers in a study group.

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Find or form a study group
Prepare questions and topics for discussion
Meet regularly to discuss and collaborate

Develop an outline for a neural network model

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Solidify your understanding of neural network architecture by creating an outline for a custom model for a specific task.

Browse courses on Neural Networks

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Define the purpose of the neural network model
Research different neural network architectures
Create a diagram of the proposed model

Build a simple multilayer perceptron model for a binary classification task

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Demonstrate your proficiency in building and training multilayer perceptron models by creating a simple model for a binary classification task.

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Define the problem and gather data
Preprocess the data
Build the neural network model
Train and evaluate the model

Contribute to an open-source project related to multilayer perceptrons or Keras

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Deepen your understanding and make a meaningful contribution to the community by contributing to an open-source project related to multilayer perceptrons or Keras.

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Find a suitable open-source project
Identify an area where you can contribute
Make a pull request to the project

Career center

Learners who complete Build Multilayer Perceptron Models with Keras will develop knowledge and skills that may be useful to these careers:

Robotics Engineer

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Computer Vision Engineer

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Natural Language Processing Engineer

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Data Engineer

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Deep Learning Engineer

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Computer Scientist

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Data Scientist

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Software Engineer

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Machine Learning Engineer

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Data Analyst

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