Shortest Path to Productivity, Google Colaboratory

Efficiency, productivity and collaboration are critical in scaling up machine learning. Being a machine learning practitioner means doing a significant amount of devops and systems integration. Enter Google Colaboratory. Its message is to "help disseminate machine learning education and research" using a Jupyter notebook environment that runs entirely in the cloud and integrated with your Google Drive.
 

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Ryan Gaspar
Will AI Change the Role of Cybersecurity?

One, around the topic of AI eliminating jobs and thoughts on how AI may change a security practitioner’s job, and two, about the possibility that AI could be misused or perhaps used by malicious actors with unintended negative consequences.

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Randy Dean
Trading Bitcoin with Reinforcement Learning

Algorithmic trading has been around for decades and has, for the most part, enjoyed a fair amount of success in its varied forms.

Reinforcement learning (RL) on the other hand, is much more "hands off." In RL, an “agent” simply aims to maximize its reward in any given environment and tries to improve its decision making through trial and error as it experiences more examples.

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Vincent Poon
Reconciliation of Financial Documents

Though financial transactions are increasingly online and digital, much business is still conducted via analog means. Companies may prefer hand filled paper documents for their security, simplicity, or familiarity. However, accounting for and reconciling such transactions across invoices, receipts, bills, and contracts with multiple formats can be extremely challenging for high volume businesses.

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Arshak Navruzyan
Self-Supervised Video Anomaly Detection

Factories could improve worker safety and reduce costs from machine, robot and worker error through incisive use of state-of-the-art deep learning techniques. Specifically, deep learning can be used to detect anomalies in video recordings of factory workers.

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Steve Shimozaki
Deep Learning Approach to Fraud

When creating a feature space for adversarial use cases like payment fraud, account takeover fraud and internal fraud, data scientists can rely on domain knowledge, intuition, personal experience and ultimately and if labeled data is available-variable selection.

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Arshak Navruzyan