Anyone who is interested in deep learning has likely gotten their hands dirty at some point playing around with Tensorflow, Google’s open source deep learning framework. Tensorflow has a lot of benefits like wide-scale adoption, deployment on mobile, and support for distributed computing, but it also has a somewhat challenging learning curve, and is difficult to debug. It also doesn’t support variable input lengths and shapes due to its static graph architecture unless you use external packages. PyTorch is a new deep learning framework that solves a lot of those problems.
PyTorch is only in beta, but users are rapidly adopting this modular deep learning framework. PyTorch supports tensor computation and dynamic computation graphs that allow you to change how the network behaves on the fly unlike static graphs that are used in frameworks such as Tensorflow. PyTorch also offers modularity, which enhances the ability to debug or see within the network. For many, PyTorch is more intuitive to learn than Tensorflow.
This talk will objectively look at PyTorch and why it might be the best fit for your deep learning use case. We’ll look at use cases that will showcase why you might want consider using Tensorflow instead.
User experience and customer support are integral to every company’s success. But it’s not easy to understand what users are thinking or how they are feeling, even when you read every single user message that comes in through feedback forms or customer support software. With Natural Language Processing and Machine Learning techniques it becomes somewhat easier to understand trends in user sentiment, main topics discussed, and detect anomalies in user message data.
A couple of weeks ago, we gave a talk about investigating user experience with natural language analysis at Sentiment Symposium and thought we’d share the talk, along with the speaker notes for anyone who is interested.
Whether you’re a scientist analyzing earthquake data to predict the next “big one”, or are in healthcare analyzing patient wait times to better staff your ER, understanding time series data is crucial to making better, data informed decisions.
This gentle introduction to time series will help you understand the components that make up a series such as trend, noise, and seasonality. It will also cover how to remove some of these components and give you an understanding on why you would want to. Some common statistical and machine learning models for forecasting and anomaly detection will be explained and we’ll briefly dive into how neural networks can provide better results for some types of analysis. Read More…
We’ve all heard about racial bias in artificial intelligence via the media, whether it’s found in recidivism software or object detection that mislabels African American people as Gorillas. Due to the increase in the media attention, people have grown more aware that implicit bias occurring in people can affect the AI systems we build.
Early this week, I was honored to give a talk on Racial Bias in Facial Recognition at PyCascades, a new regional Python conference. Last week I wrote a blog post on learning facial recognition through OpenFace where I went into deeper detail about both facial recognition and the OpenFace architecture, so if you want to give that a read through before checking out this talk, I highly encourage it. Read More…
Facial recognition has become an increasingly ubiquitous part of our lives.
Today smartphones use facial recognition for access control while animated movies such as Avatar use it to bring realistic movement and expression to life. Police surveillance cameras use face recognition software to identify citizens that have warrants out for their arrest and these models are also being used in retail stores for targeted marketing campaigns. And of course we’ve all used celebrity look-a-like apps and Facebook’s auto tagger that classifies us, our friends, and our family.
Face recognition can be used in many different applications, but not all facial recognition libraries are equal in accuracy and performance and most state-of-the-art systems are proprietary black boxes.
OpenFace is an open source library that rivals the performance and accuracy of proprietary models. This project was created with mobile performance in mind, so let’s look at some of the internals that make this library fast and accurate and think through some use cases on why you might want to implement it in your project. Read More…