The infamous data science workflow with interconnected circles of data acquisition, wrangling, analysis, and reporting understates the multi-connectivity and non-linearity of these components. The same is true for machine learning and deep learning workflows. I understand the need for oversimplification is expedient in presentations and executive summaries. However, it may paint unrealistic pictures, hide the intricacies of ML development and conceal the realities of the mess.

Audio signals are all around us. As such, there is an increasing interest in audio classification for various scenarios, from fire alarm detection for hearing impaired people, through engine sound analysis for maintenance purposes, to baby monitoring. Though audio signals are temporal in nature, in many cases it is possible to leverage recent advancements in the field of image classification and use popular high performing convolutional neural networks for audio classification.

The design and training of neural networks are still challenging and unpredictable procedures. The difficulty of tuning these models makes training and reproducing more of an art than a science, based on the researcher’s knowledge and experience. One of the reasons for this difficulty is that the training procedure of machine learning models includes multiple hyperparameters that affect how the training process fits the model to the data.

So, if you’re a nose-to-the-keyboard developer, there’s ample probability that this analogy is outside your comfort zone … bear with me. Imagine two Olympics-level figure skaters working together on the ice, day in and day out, to develop and perfect a medal-winning performance. Each has his or her role, and they work in sync to merge their actions and fine-tune the results.

Neural Guard produces automated threat detection solutions powered by AI for the security screening market. With the expansion of global trends like urbanization, aviation, mass transportation, and global trade, the associated security and commercial challenges have become ever more crucial.

I’ll admit it. I am a gushing fan of this new product from Allegro AI called Allegro Trains. I’m not sure what to call it — what noun I should attach to this creature. “Framework” and “Platform” have become, to my ears, rather meaningless jargon designed to detach suit-wearing types from their money. “Harness” is close.

If your company has already started getting into machine learning / deep learning, you will quickly relate to the following story. If your company is taking its first steps into data-science, here is what is about to be dropped on you. If none of the above strikes a chord, well it’s probably good to know what’s out there because data-science is all the rage now, and it won’t be long until it gets you too 🙂

We are all familiar with this scenario, you work on your training code, fix “all” of the bugs (the ones you know about), wait for a few iterations, see that batch size wasn’t wrong and nothing blows up, and then you happily go home. However, when you come back into the office the next day look at your loss and test accuracy you’re horrified to find that the experiment crashed on the first test cycle because you pointed your test set in the wrong folder 🙁

The recent global pandemic caused by the COVID-19 virus has threatened the sanctity of our humanity and the well-being of our societies at large. Similar to times of war, the pandemic has also given us the opportunity to appreciate the things we take for granted such as health workers, food suppliers, drivers, grocery store clerks and many others who are in the frontlines keeping us safe at this difficult time, Salute!

This blog post is a first of a series on how to leverage PyTorch’s ecosystem tools to easily jumpstart your ML / DL project. The first part of this blog describes common problems appearing when developing ML / DL solutions, and the second describes a simple image classification example demonstrating how to use Allegro Trains and PyTorch to address those problems.