loading reading lists ...
');
function adjustReadingListIcon(isInReadingList){
$readingListToggle.toggleClass("fa-plus", !isInReadingList);
$readingListToggle.toggleClass("fa-check", isInReadingList);
var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list";
$readingListToggle.attr("title", tooltipMessage);
$readingListToggle.attr("data-original-title", tooltipMessage);
}
$.ajax({
url: "/readingList/isInReadingList",
data: {
productId: 2288
}
}).done(function (data) {
adjustReadingListIcon(data && data.hasProductInReadingList);
}).catch(function(e){
console.log(e);
adjustReadingListIcon(false);
});
$readingListToggle.on("click", function(){
if(codePromise == null){ showToast() }
loadCode().then(function(store){
store.requestReadingListSpecificationForProduct({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId,
title: window.readingListsServerVars.title
});
ReadingLists.ReactDOM.render(
ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, {
store: store,
}),
document.getElementById("reading-lists-modal")
);
}).catch(function(e){
console.log("Error loading code reading list code");
});
});
var codePromise
var readingListStore
function loadCode(){
if(codePromise) { return codePromise }
return codePromise = new Promise(function (resolve, reject){
$.getScript(window.readingListsServerVars.libraryLocation).done(function(){
hideToast()
readingListStore = new ReadingLists.ReadingListStore(
new ReadingLists.ReadingListProvider(
new ReadingLists.ReadingListWebProvider(
ReadingLists.SourceApp.marketplace,
getDeploymentType()
)
)
);
readingListStore.onReadingListChange(handleChange);
readingListStore.onReadingListModalChange(handleChange);
resolve(readingListStore);
}).catch(function(){
hideToast();
console.log("Error downloading reading lists source");
$readingListToggle.css("display", "none");
reject();
});
});
}
function handleChange(){
if(readingListStore != null) {
adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId
}));
}
}
var $readingListToast = $("#reading-list-toast");
function showToast(){
$readingListToast.css("display", "flex");
setTimeout(function(){
$readingListToast.addClass("shown");
}, 16);
}
function hideToast(){
$readingListToast.removeClass("shown");
setTimeout(function(){
$readingListToast.css("display", "none");
}, 150);
}
function getDeploymentType(){
switch(window.readingListsServerVars.deploymentType){
case "development":
case "test":
return ReadingLists.DeploymentType.dev;
case "qa":
return ReadingLists.DeploymentType.qa;
case "production":
return ReadingLists.DeploymentType.prod;
case "docker":
return ReadingLists.DeploymentType.docker;
default:
console.error("Unknown deployment environment, defaulting to production");
return ReadingLists.DeploymentType.prod;
}
}
});
}
});
- prerequisites
- basics of Python • basics of pandas • basics of scikit-learn • basics of machine learning • basics of Fast.ai
- skills learned
- selecting, cleaning and choosing data for collaborative filtering • matrix collaborative filtering techniques using Fast.ai collab_learner class • learning latent factors for collaborative filtering recommenders
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
In this liveProject, you’ll create a recommendation engine for an online store using the Fast.ai library. You’ll utilize a dot product and a neural network to come up with the latent factors in a rating matrix, and compare and contrast them to determine which is likely to deliver the best recommendations. You’ll need to select and clean your data, pick the right methods, then create the functions that you need to recommend products based on predicted ratings.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
features
Fast.ai project for free
