Entries by Matteo Alberti

Functional programming for deep learning

Author: Joyce Xu Before I started my most recent job at ThinkTopic, the concepts of “functional programming” and “machine learning” belonged to two different worlds entirely. One was a programming paradigm surging in popularity as the world turned towards simplicity, composability, and immutability to maintain complex scaling applications; the other was a tool to teach computers to autocomplete doodles and make […]

How to get a job as a Data Scientist?

Author: Favio Vázquez     Hi everyone. This blog post comes from 3 post I did recently at LinkedIn. Here they are Part 1, Part 2, and Part 3. This is a hard question to answer. Hang with me in this one (and this is not the final answer about the universe, existence and everything). This is one […]

Deep Learning Italia – Roma – Luglio 2018

1) Francesco Pugliese – Co-Founder Deep Learning Italia | Ricercato ISTAT “Introduzione divulgativa alle Reti Neurali e al Deep Learning”. Abstract: Descrizione introduttiva del modello del neurone artificiale e confronto con il neurone biologico, la storia delle reti neurali e come si è arrivati oggi al Deep Learning, detrattori vs sostenitori delle reti neurali e […]

Convolutional autoencoders features visualization

Cristiano De Nobili – Fisico   “Convolutional autoencoders features visualization” Le reti CNN formano delle rappresentazioni a ogni livello, le feature maps, e i livelli più lontani dal livello di input contengono features via via sempre più complesse, che catturano caratteristiche che descrivono grandi porzioni dell’immagine di input. In questo talk faremo vedere una tipica […]

Introduzione al deep learning

Daniele D’Armiento – Physicist presso Samsung “Introduzione al deep learning ed alla piattaforma Deep Learning Italia” Abstract: Illustrazione della community Deep Learning Italia e degli strumenti oggi sviluppati e a disposizione della community. Introduzione alle features del sito www.deeplearningitalia.com: i tutorials, il question & answer, le references, gli sviluppi futuri

Deep Learning for Object Detection: A Comprehensive Review

Author Joyce Xu         With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around […]

An intuitive guide to deep network architectures

Author: Joyce Xu     Over the past few years, much of the progress in deep learning for computer vision can be boiled down to just a handful of neural network architectures. Setting aside all the math, the code, and the implementation details, I wanted to explore one simple question: how and why do these […]

Introduction to Autoencoders

Author: Nathan Hubens Linkedind: https://www.linkedin.com/in/nathan-hubens Autoencoders (AE) are neural networks that aims to copy their inputs to their outputs. They work by compressing the input into a latent-spacerepresentation, and then reconstructing the output from this representation. This kind of network is composed of two parts : Encoder: This is the part of the network that compresses the input into […]

Using Deep Learning to improve FIFA 18 graphics

Author: Chintan Trivedi       Comparison of Cristiano Ronaldo’s face, with the left one from FIFA 18 and the right one generated by a Deep Neural Network. Game Studios spend millions of dollars and thousands of development hours designing game graphics in trying to make them look as close to reality as possible. While the […]

Building a Deep Neural Network to play FIFA 18

Author: Chintan Trivedi Linkedin: https://www.linkedin.com/in/chintan-trivedi-78665774/   A.I. bots in gaming are usually built by hand-coding a bunch of rules that impart game-intelligence. For the most part, this approach does a fairly good job of making the bot imitate human-like behavior. However, for most games it is still easy to tell apart a bot from an actual human […]