Armando Vieira

24 Abonné

Entrepreneur, scientist and machine learning enthusiast. Recently engaged in applying deep learning networks for hard problems on classification and regression. Writing a book "Business Applications of Deep Learning" to be released in 2017. More info on my website: armando.lidinwise.com. Author of more than 70 publications on international journals like "Expert Systems with Applications", "Journal Applied Physics", "Neurocomputing" and "Physical Review E". Co-creator of a new algorithm to classify high-dimensional data (HLVQ). More on ResearchGate profile: https://www.researchgate.net/profile/Armando_Vieira3 Passionate and experienced on Predictive Analytics and Big Data using Machin...

machine learning big data telecom analytics

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Armando Vieira

Présentations

Customer insights from telecom data using deep learning

Data Science Strategy

AI - history and recent breakthroughs

Documents

graph_embeddings

J’aime

Chatbots rpa-ia

The State of Artificial Intelligence in 2018: A Good Old Fashioned Report

TensorFlow London 14: Ben Hall 'Machine Learning Workloads with Kubernetes and Kubeflow'

Generative Adversarial Networks and Their Applications

A (Very) Gentle Introduction to Generative Adversarial Networks (a.k.a GANs)

Frank Chen at AI Frontiers: Startups and AI

Rocket fuel cross device and ptv 12-9-15 sharedv2

All about Programmatic buying(RTB), DSP,SSP, DMP & DCT - A complete digital Advertising world

Design Patterns for Machine Learning in Production - Sergei Izrailev, Chief Data Scientist, BeeswaxIO

Smart Applications with Machine Learning by H2O - Alex Tellez of RHalf speaks at H2OWorld2014

Deep Learning: a birds eye view

Multimodal Deep Learning (D4L4 Deep Learning for Speech and Language UPC 2017)

Value iteration networks

Image-to-Image Translation with Conditional Adversarial Nets (UPC Reading Group)

Hierarchical Object Detection with Deep Reinforcement Learning

Causal inference in data science

Machine Learning - Challenges, Learnings & Opportunities

Deep Learning and the state of AI / 2016