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Julio Souza (@juliocnsouzadev)

juliocnsouzadev

Stars
167
Global Rank 141,851 (Top 5 %)
Followers 19
Following 19
Registered over 11 years ago
Most used languages

Java
29.5 %

Jupyter Notebook
17.0 %

JavaScript
13.6 %

Go
9.1 %

HTML
6.8 %

Python
5.7 %

Scala
3.4 %

HCL 2.3 %

TypeScript
2.3 %

XSLT 2.3 %

Dockerfile
1.1 %

CSS
1.1 %

Clojure
1.1 %

Smarty 1.1 %

MATLAB
1.1 %

ApacheConf 1.1 %

C
1.1 %
Location 🇬🇧 United Kingdom
Country Total Rank 6,412
Country Ranking

ApacheConf 42

XSLT 75

Smarty 102

HCL 383

Scala
499

MATLAB
579

Clojure
621

Jupyter Notebook
725

Java
878

Dockerfile
1,047

Go
1,081

HTML
1,431

TypeScript
3,431

CSS
3,550

JavaScript
5,100

C
5,382

Python
8,912

machine_learning_stanford

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

puzzles_java

Algorithms problems solving

deep-learning

Jupyter Notebook

omg_mongodb

Lab OGM usando Hibernate e MongoDB

pneumonia-predictor

Predictions made by a Tensorflow Deep Learning Model trained on Kaggle Dataset: Chest X-Ray Images (Pneumonia)

gcp

GCP: Complete Google Data Engineer and Cloud Architect

Jupyter Notebook

html_builder

A library for writing html files in java, usefull for creating emails dynamically.

puzzles_go

data-analysis

Jupyter Notebook

terraform-labs

CKAD

CKAD - CERTIFIED KUBERNETES APPLICATION DEVELOPER

spring-microservices-kubernetes

observability

esus_thrift_mapped_conversion

Lib for mapping data for integrate data with ESUS through Apache Thrift.

spring-stackdriver

machine-learning

Shallow Machine Learning

gcp-data-engineer

Jupyter Notebook

airflow-labs

notebooks

Notebooks for ML and Data Analysis

Jupyter Notebook

california-housing

go_booking_app

Simple CLI booking app

lib-bag-of-words-pre-process

liferay_lab

Liferay Learning Path with OSGi, Portlets, Soy and Metaljs

nlp

Jupyter Notebook

react-material

android_examples

android_exemples

graph_with_spark

Hands of labs Building a Graph with Spark using ClouderaVM

go_design_patterns

go_basics

katacoda-scenarios

Katacoda Scenarios

spring-books-store

Spark-Scala

ocpjp

Codes from OCPJP preparation

GCP-Migrating-from-Spark-to-BigQuery-via-Dataproc

Migrating from Spark to BigQuery via Dataproc

Jupyter Notebook

fashion-mnist-api

spark-java

Spark using Java

ecommerce-purchases-analysis-pandas

Analysis of a ecommerce purchases dataset with pandas

Jupyter Notebook

terraform-101

puzzles_sql

es6

OOP and Design Patterns with Javascript

spark-python

Jupyter Notebook

gdg_intro_angularjs4

todo_metaljs

sf_salaries_pandas

Explore San Francisco city employee salary data with Pandas

Jupyter Notebook

machine-learning-apis-gcp

It’s no secret that machine learning is one of the fastest growing fields in tech, and the Google Cloud Platform has been instrumental in furthering it’s development. With a host of APIs, GCP has a tool for just about any machine learning job. In this advanced-level quest, you will get hands-on practice with machine learning APIs by taking labs like Implementing an AI Chatbot with Dialogflow and Detect Labels, Faces, and Landmarks in Images with the Cloud Vision API.

Jupyter Notebook

boston_housing

O mercado imobiliário de Boston é altamente competitivo, e você quer ser o melhor corretor de imóveis da região. Para competir com seus colegas, você decidiu elencar alguns conceitos básicos de Machine Learning para ajudar você e seu cliente a acharem o melhor preço de venda para a casa dele. Com sorte, você se deparou com o conjunto de dados de habitação de Boston, que contém dados agregados de várias características para casas das comunidades da Grande Boston, incluindo o valor médio das casas para cada uma das regiões. Sua tarefa é construir um modelo ótimo, baseado na análise das estatísticas com as ferramentas disponíveis. Esse modelo será, então, usado para estimar o melhor preço de venda para a casa de seus clientes.

customer_segments

Uma distribuidora atacadista recentemente testou uma mudança em seu método de entrega para alguns clientes, trocando de um serviço de entrega cinco vezes por semana pela manhã para um serviço de entrega mais barato, três vezes por semana ao final da tarde. Teste iniciais não mostraram nenhum resultado insatisfatório significante, então eles implementaram a opção mais barata para todos os clientes. Quase imediatamente, a distribuidora começou a receber reclamações sobre as mudanças no serviço e clientes começaram a cancelar entregas — perdendo mais dinheiro do que estava sendo poupado. Você foi contratado pela distribuidora para descobrir que tipos de clientes eles têm, para que possam fazer decisões melhores e mais bem informadas no futuro. Sua tarefa é utilizar técnicas de aprendizagem não-supervisionada para ver se existe qualquer similaridade entre os clientes, e como melhor segmentá-los em categorias distintas.

Jupyter Notebook

student_intervention

Com o aumento do uso da tecnologia na educação, uma vasta quantidade de dados foi disponibilizada para avaliação e predição. Registros das atividades dos alunos, notas, interações com os professores e colegas, entre outros, agora são captados em tempo real por sistemas de gerenciamento de aprendizagem como o Canvas e o Edmodo. Esse fenômeno é particularmente forte em aulas à distância, que estão tornando-se populares até mesmo no Ensino Fundamental e Médio. Dentro de todos os níveis da educação, existe um impulso para aumentar a probabilidade de sucesso do aluno sem diluir a educação nem encorajar comportamentos que não vão melhorar os problemas na raiz. A taxa de graduação frequentemente é o critério preferencial, e os educadores procuram novas maneiras de predizer o sucesso ou o fracasso dos estudantes cedo o bastante para organizar intervenções eficazes.

m101js

Codes created during M101JS (Mongo for NodeJS Developers)

clojure_starting

javaoo_simple_demo

Simple RPG game to show OO concepts with Java

tiffus

Automatically exported from code.google.com/p/tiffus

nats_websocket_poc

juliocnsouzadev

AngulaJSExample

Exemplo criados durante o estudo do livro AngulaJS na Pratica

oca

Exemplo criados durante a preparação para OCA Java SE 7 Programmer I

simple_msg_timeline

MySimpleTwiiter is a MEAN Stack app that hava some microblogs features.

kafka-ish

django_labs

puzzles_py

eip

Enterprise Integration Patterns Examples

vagrant_puppet_labs

Estudo inicial de aprimoramento de DevOps com Vagrant e Puppet

jasmine_karma_lab

Testes Unitários com Jasmine e Karma

java8

mlnd_spam_detecter

Implementação do algoritmo Naive Bayes para detectar mensagens de texto de spam.

Jupyter Notebook

design_patterns

Códigos dos Posts sobre Design Patterns do Blog

ionic_labs

gdg_intro_angularjs4_server

sun_earth

Um sistema solar feito usando html5 e css3

filtering_angularjs

Simple AngularJS App with Bootstrap showing use of filtering data

racha_manager

App Android para gerenciar rachas (peladas) de futebol

inferential_statistics_project

Jupyter Notebook

c_labs

docker_101

android_annotations_lab

Simple Android App for start working with Android Annotations

scala101

Starting with Scala Basic Principles

microservices_nlp

Building highly available, scalable, resilient distributed applications using Go

cdi

puzzles_js

hadoop_joining_data

sorteador-javafx

spark_with_scala

Apache Spark 2.0 with Scala - Hands On with Big Data!

word_count_hadoop_python

Simple Word Count MapReduce code in Python

maven-github-docker-push-example

descriptive_statistics_project

Jupyter Notebook

leader_election

Demo of Leader Election Concepts

angular2

m102

simple_orm

Simple ORM is lab that simulates the behavior of JPA in Java by Mapping Objects to Relational Databases.

JerseyRestAPI-with-SpringBoot

A Spring Boot app with Restful API for accessing and modifying data stored in an embedded database HSQL.

jumper

scala_datascience

Data Scientists tend to favor one of three programming languages, Python, R, or Scala. Scala, short fore scalable language, is the future ready. Move beyond your regular java, and check out what is brewing in this language.

java-google-translate-text-to-speech

Automatically exported from code.google.com/p/java-google-translate-text-to-speech

mean_stack

A JS end to end application using MongoDB, Express, AngularJS and NodeJS.