Tag: machine-learning

How tech is transforming the intelligence industry

Shay Hershkovitz Contributor Shay Hershkovitz is a Senior Research Fellow at The Intelligence Methodology Research Center (IMRC). At a conference on the future challenges of intelligence organizations held in 2018, former Director of National Intelligence Dan Coats argued that he...

/ August 11, 2019

How We’re Using Machine Learning and Trading Bots to Predict Crypto Prices

W e just launched AlgoHive, an open-source project to crowdsource the prediction of cryptocurrency prices and automate crypto trading. We are now sharing our vision towards where our project is headed. In short we’d like to make our group learnings...

/ November 17, 2018

How to give 3x boost to Apache Spark ML using FPGAs & without a single line of code

Originally posted by Chris Kachris on InAccel blog Emerging cloud applications like machine learning, AI and big data analytics require high-performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many...

/ October 27, 2018

Boost your face recognition accuracy with this quick step

I admit, we’ve made it almost too easy to deploy a state-of-the-art face recognition machine learning model. So easy in fact, the only thing you need to know to try it for yourself is how to copy and paste commands...

/ October 21, 2018

How to unwrap wine labels programmatically

How to unwrap wine labels programmatically In our application, there is a feature like in Vivino?—?wine detection by label picture. Under the hood, we use third-party services, Tineye?—?to detect the best matching label, Google Vision?—?to read text on it. The...

/ September 28, 2018

Optimizing a Portfolio of Cryptocurrencies with Deep Reinforcement Learning

Portfolio Optimization or the process of giving optimal weights to assets in a financial portfolio is a fundamental problem in Financial Engineering. There are many approaches one can follow?—?for passive investments the most common is liquidity based weighting or market...

/ September 17, 2018

Machine learning — Is the emperor wearing clothes?

A behind-the-scenes look at how machine learning works Machine learning uses patterns in data to label things. Sounds magical? The core concepts are actually embarrassingly simple. I say “embarrassingly” because if someone made you think it’s mystical, they should be embarrassed....

/ September 15, 2018

Personal Assistant Kino Part 4 — Smart Feed

Smart Feed I wanted to automate my pattern that check new articles, put them in Pocket, read carefully and move to favorite category. This is why Smart Feed function was created. First, the RSS urls are required for this function....

/ September 13, 2018

Dominic Monn: DL Practitioner Interview #1

Today we’re talking with Dominic Monn. A Great Leader, Founder, Community Leader, Self Driving Car Engineer and currently a DL Engineer working Remotely. Sanyam:? Hey Dominic! Thank you for doing this interview. I’m thrilled to be interviewing you. Dominic: Hey...

/ September 10, 2018

Machine Learning basics — It’s your cup of tea!

Supervised Learning Let’s look at the two task of supervised Learning Regression Classification SUPERVISED LEARNING = DATA SET CONTAINING TRAINING EXAMPLES WITH ASSOCIATED CORRECT LABELS Supervised Learning is a function that maps an input to an output based on example...

/ September 10, 2018

Populations — You’re doing it wrong

Why lawyers might be better than you at statistics A population can be people, pixels, pumpkins, pokemon, or whatever else strikes your fancy. It’s whatever you’ve chosen to be interested in for the purpose of your analysis. Let’s cover the...

/ September 8, 2018

Machine Learning Model Pipelines: Part I

Photo by Patrick Hendry on Unsplash AI systems are moving from development and testing into production. At their heart, AI systems are simply computation and therefore deploying a system remotely provides remote computation. If this system involves multiple steps of computation,...

/ September 1, 2018

Chatbot Development Challenges?-?Part 1

Last October, virtual assistants were placed at the Peak of Inflated Expectations in 2017 Gartner Hype Cycle for Emerging Technologies. It was a substantial jump from the middle of the Innovation Trigger part of the cycle in 2016. Around the...

/ August 22, 2018

Adversarially generated Julia sets

I thought I’d follow up my first article/tutorial about Julia, by showcasing another side of the language’s ecosystem, libraries for machine learning. Namely, I’ll be displaying Knet and AutoGard, by implementing an image generating adversarial network. For the sake of...

/ August 21, 2018