In this blog we walk-through how to train an autoencoder model using a dataset of customers with good financial profiles that are seeking loans. The autoencoder will learn the common traits that make a customer a “good” credit risk.
Modeling is about understanding behavior. It’s mostly applied towards commercial goals, but it will warm your heart and fill it with purpose when you apply it towards helping real people and especially our youth. Applied data science requires good data and good modeling skills but also a lot of pre- and post- analysis and workflow planning. This can go a long way in not only identifying who is at risk but tailoring the best intervention to help, in our case students, get back on track. And that’s the big picture.
By now we should know that machines tend to uplift our quality of life. Over the past 100 years or so, the unfolding of each new breakthrough technology — from electricity to the automobile to the internet — has brought trepidation, then ultimately acceptance. Despite these past experiences, our society seems to have a peculiarly strong resistance to the latest technologies that are radically transforming our lives: artificial intelligence (AI) and machine learning (ML).
Women in Tech, We Need More to Join the Machine Learning Revolution Read More »
Society’s Mentality is Holding AI Back. How to Change Hearts & Minds Read More »
Looking for Anomalies in Stock Market Data with Conditional Variational AutoEncoders (CVAE) and TensorFlow Just like Fast R-CNN and Mask-R CNN evolved from Convolutional Neural Networks (CNN), Conditional Variational AutoEncoders (CVAE) and Variational AutoEncoders (VAE) evolved from the classic AutoEncoder. CVAEs are the latest incarnation of unsupervised neural network anomaly detection tools offering some new and …
It was lunchtime and wonton Tuesday at the cafeteria, but our data scientist was not there. He was far away with dirt smeared over his face and hiding behind a stack of red construction bricks trying to blend in. It was a hot day, like looking for shade under a magnifying glass kind of hot. His …
The Guerrilla Data Scientist and that “Can Do” Attitude Read More »
Pairing Q-Learning with other ML concepts like Reinforcement Learning (RL) and Case-based Reasoning (CBR) has shown a significant increase in the accuracy of a model. With the rise in popularity of Internet of Things (IoT), there are plenty of third-party sensor attachments, hardware and software management tools to leverage. The hope is to analyze the data after …
Using Reinforcement Learning to build an Emergency Response Scenario Read More »
Chatbots are all the rage these days and we get a lot of requests for them at SpringML. They not only have that “AI” chic, they’re also a hope for faster customer service at a much cheaper cost – a huge win-win. Though chatbot technology is mature and available today, see Dialogflow from Google as an …
Building Intents From Customer Service Transcripts Read More »
Google Video Intelligence and Vision APIs – A Powerful & Fun Combination – Recognizing Actors in Near Real Time Just in time for the holiday lull, here is a fun ML project that processes a popular movie-clip down to the actor’s face and biography in a fully automated pipeline and in near real time! It’s …
Google Video Intelligence – Recognizing Actors in Near Real Time Read More »
