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-data-stream-development-with-apache-spark-kafka-and-spring-boot

Data Stream Development with Apache Spark, Kafka and Spring Boot by Packt Publishing

.github

GitHub configuration

01-aulasdatascience

02456-deep-learning-with-pytorch

Exercises and supplementary material for the deep learning course 02456 using PyTorch.

10-twinstick

A cross-platform, twin stick control game set in 2.5D as part of The Complete Unity Developer.

100-days-of-code

Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)

100daysofmlcode

100 days of ML code challenge

168-multi-file-upload-angular-firebase

Episode 168 - Upload Multiple Files Concurrently to Firebase Storage

192-flutter-fcm-push-notifications

Episode 192 - Flutter Firebase Cloud Messaging Guide

1972

1972 - Criando APIs com NodeJs

2-page-open-book

Corona Lua 2-page open book

202-chatbot-dialogflow

Episode 202 - Build a chatbot with Dialogflow, Cloud Functions, and Angular

4hrjamchoplifter

62-firestore-infinite-scroll

Episode 62 - Infinite scroll pagination Firestore

63-firestore-aggregation

Episode 63 - Firestore Data Aggregation

64-push-messaging-firestore

Episode 64 - FCM with Firestore

69-csv-export-cloud-function

69-csv-export-cloud-function

76-material-datatable-firestore

Episode 76 - Realtime Angular Material Datatables with Firestore

83-ionic-not-hotdog

Use Ionic, Firebase, and Google Cloud Vision to build the Not Hotdog app from HBO's Silicon Valley

86-firestore-threaded-comments

Advanced Firestore Data Modeling Example - Threaded Comments

87-reactive-forms-async-validation-firestore

Episode 87 - Validate username uniqueness in Firestore with Angular Reactive Forms

89-firestore-typeahead-autocomplete

Episode 89 - Build a Typeahead form with Firestore and Angular

97-tensorflowjs-quick-start

Episode 97 - TensorFlow.js Quick Start

a7crud

aas

Code to accompany Advanced Analytics with Spark from O'Reilly Media

abelsiqueira.github.io

My site

ability-system-course

absinthe

The GraphQL toolkit for Elixir

abusniffv1

Adversaries leverage social network friend relationships to collect sensitive data from users and target them with abuse that includes fake news, cyberbullying, malware, and propaganda. Case in point, 71 out of 80 user study participants had at least 1 Facebook friend with whom they never interact, either in Facebook or in real life, or whom they believe is likely to abuse their posted photos or status updates, or post offensive, false or malicious content. We introduce AbuSniff, a system that identifies Facebook friends perceived as strangers or abusive, and protects the user by unfriending, unfollowing, or restricting the access to information for such friends. We develop a questionnaire to detect perceived strangers and friend abuse. We introduce mutual Facebook activity features and show that they can train supervised learning algorithms to predict questionnaire responses.