Category: Data Science

Charlie Javice Indicted for $175 Million Frank Fraud

Frank was a financial services application which made it easier for students to fill out the FAFSA. Last year JPMorgan Chase filed a lawsuit against Charlie Javice, the founder of Frank alleging that she had misrepresented the number of users and suing her for fraud. Charlie Javice has now had an opportunity to file her counterclaim and argue about why her actions were not fraud. After the claim and counterclaim was filed Charlie Javice ended up indicted in the Southern District of New York for wire fraud and bank fraud charges.

$175 Million and Four Million Fake Users: The Story of Frank’s Fraud

https://youtu.be/PEG9fRVykCE Frank was a financial services application which made it easier for students to fill out the FAFSA. Last year JPMorgan Chase filed a lawsuit against Charlie Javice, the founder of Frank alleging that she had misrepresented the number of users and suing her for fraud. Charlie Javice has now had an opportunity to file … Continue reading $175 Million and Four Million Fake Users: The Story of Frank’s Fraud →

Sunday Reads – Links I Found Interesting 6/16

PyTorch PyTorch Hub is an awesome new step forward in research reproducibility for machine learning and artificial intelligence. Makes it super easy to publish your pre-trained research models and for others to download them and test them. I was shocked how uncommon it was for people to publish their models when I first started reading … Continue reading Sunday Reads – Links I Found Interesting 6/16 →

Sunday Reads – Links I Found Interesting 6/9

Mapping human microbiome drug metabolism by gut bacteria and their genes A fascinating look at how the microbiome may affect drug metabolism. Important to remember that the game does not end at pharmacogenomics and we need to be paying attention to the complex interplay of numerous complex systems to understand drug action. Deep learning can … Continue reading Sunday Reads – Links I Found Interesting 6/9 →

Sunday Reads – Links I Found Interesting 6/2

EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling Google did that thing they do again where they make vast steps in artificial intelligence and machine learning. Efficiency of these image recognition networks is up to 10* greater. Most of this gain is because they try to use "scaling coefficients" so that the network … Continue reading Sunday Reads – Links I Found Interesting 6/2 →