Icaro Artificial Intelligence
Deep Learning Italia is the biggest Italian Community of Deep Learning. The main purpose is to share knowledge (tutorials, articles and courses) on Machine Learning and Deep Learning.
[Deep Learning Italia is an AI factory]
Icaro Artificial Intelligence is a Fintech company with a focus on Deep Learning Solutions for cryptocurrencies marketplace. Icaro AI can help the trader for the decision-making and find a new pattern for your investment strategy.
[Deep Learning for Cryptocurrencies Trading]
I attended in Kaggle Competition "Data Science Bowl 2017" with my team. We got awarded a bronze star.
-Speaker at Luiss Enlabs, Deep Learning and Computer Vision 3 May 2017
-Speaker at International Summer School of Deep Learning on Bilbao 17/21 July 2017. My speech was about
"Sentiment Analysis tool using RCNN".
- Singularity U Italy Summit 27/28 Milan 2017
- Guest at FintechStage Madrid 12 December, 2017
- Speaker at Deep Learning and Finance, London 15-16 March 2018
- Speaker at World Congress on Data Science and Big Data Analytics May 24-25, 2018 Toronto, Canada.
Abstract: Sentiment Analysis + a Tool for Data Analysis and Discovery.
My main research topics are: Cloudera, Deep Learning, Neuroscience, Health, Fintech
Speeches di Matteo Testi
Volatility: your Enemy or your Friend? A Deep Learning Solution
Cryptocurrency time series are usually known to be very complex, non-stationary and very noisy. Furthermore, in recent years, more and more Deep Learning models have been employed to Sentiment Analysis (SA) thanks to their automatic high-dimensional feature extraction capability even from non-structured symbolic data such as generic texts (i.e. tweet). These breakthroughs are also due to the latest pre-processing discovers like Word Embeddings (Mikolov, 2013). In this work we propose a novel stack of cutting-edge Deep Learning models aimed to generate outperforming prediction accuracy and profitability in cryptocurrency forecasting. Namely, we leverage a new Deep Learning time-series model called WSAEs-LSTM by efficiently combining Wavelet-Trasforms (WT) for data denoising; Stacked Autoencoders (SAE) for high-level feature extraction and LSTM (Long Short Term Memory) for modelling cryptocurrency prices historical data. On the other side, a Recurrent Convolutional Neural Network model (RCNN) achieving the state-of-art accuracy in sentiment classification, has been implemented (Lai, et al., 2015). Our RCNN implementation achieves 85% of accuracy on this training set. Using RCNN we produce a sentiment time-series for each textual sequence which is eventually inserted as input to WSAEs-LSTM. The high accuracy of RCNNs and WSAEs-LSTM enables us to reach supreme levels of crypto-coin predictions by measuring RMSE (and MAPE) between real values of close and volume attribute and the predicted ones, as far as the -test set is exclusively concerned. The historical data entered into WSAEs-LSTM (other than the Sentiment time-series) are high, low, open, close, volume, weighted average attributes of 63 different cryptocurrencies.Lingua speech: Italian