Using C++ and CUDA Extensions to write high performance kernels in PyTorch — This article describes how to use Torch’s CUDA extension library to write high performance kernels for PyTorch modules. Background Occasionally, you may need to process a tensor (transform or apply a kernel) that isn’t in PyTorch’s standard library. You could detach the tensor, perform the transformation, then move it back to…

Applying Kalman Filters to real-time streaming data — In this article I present a method for using Kalman Filters on real time streams. Providing a true state estimate for a real time system is useful for a number of applications. Here are two examples: Tracking the actual values for highly sensitive sensors Tracking the velocity of objects with…

Identify the trend in univariate time series data using statistics! — In this article I discuss using Mann Kendall methods to automatically identify the trend in a time series. This method will allow you to automatically 1) identify if a trend exists and 2) determine the strength of that trend using a statistical approach. This will not decompose the time series…

A visual method for exploring natural clusters in transcribed speeches — In this article, I demonstrate a method for understanding natural clusters of statements in transcribed speeches. This is a useful way to understand latent themes in public speeches or other long form transcribed audio data. Additionally, I demonstrate how to visualize this data easily with Streamlit. There are a few…

A summary of how to get it done: taking ideas and putting them into production — I generally have lots of ideas when I’m thinking about machine learning. I dream up new architectures and new methods all the time, but often find myself with a combinatorial explosion of ideas to test. If you’re a researcher, there are likely 5–10 different ideas you are working with in…

Using information theory on natural language — In this article, I demonstrate how to quantify the amount of information in a single statement, or sentence from a corpus of documents using principles of information theory. This method can be used in situations where one needs to understand the “interestingness” of a statement from a basket of statements…

A short primer on scaling up your deep learning to multiple GPUs — In this multipart article, I outline how to scale your deep learning to multiple GPUs and multiple machines using Horovod, Uber’s distributed deep learning framework. Read part one here: High Performance Distributed Deep Learning with multiple GPUs on Google Cloud Platform — Part 1 Part 1 of short series outlining how to scale up your machine learning experiments to multiple GPUssamsachedina.medium.com

A short primer on scaling up your deep learning to multiple GPUs — In this multipart article, I outline how to scale your deep learning to multiple GPUs and multiple machines using Horovod, Uber’s distributed deep learning framework. Part 1 — Setting up the experiment and laying the foundation Deep neural networks have reached a size in which training on a single machine can take multiple days to weeks (or more!). …

A tensor’s journey through an LSTM Layer visualized — In building a deep neural network, especially using some of the higher level frameworks such as Keras, we often don’t fully understand what’s happening in each layer. …