Build and train a multilayer perceptron (MLP) with Keras
Perform topic classification with neural networks
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.
The prerequisites for the project are prior Python programming experience as well as a theoretical understanding of neural networks.
Project Overview and Import Libraries
Load the Reuters Dataset
Vectorize Sequences and One-hot Encode Class Labels
Build Multilayer Perceptron Model
Evaluate Model on Test Data
Professor taught course quite well and work load was bearable. Though it was soooooo easy course I would suggest Professor to increase the difficulty level by adding another week.
Nice project for practice. For those who are beginner it is very good for them to do practice.
Nice project, could be a bit better with more written instructions. But still, learnt a lot!
easty-to-use, fast project accompanied by a general understanding of MPLs!
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