In this tutorial, we start by introducing techniques for extracting audio features from music data. We then show how to implement a music genre classifier from scratch in TensorFlow/Keras using those features calculated by the Librosa library.
In our newest tutorial, we discuss the history of image dehazing, show how to set an image dehazing task up in a notebook, and then examine 7 different techniques for performing image dehazing with deep learning!
In this blog post, we learn how to build an unsupervised NLP pipeline for automatically extracting/generating glossaries and associated definitions from a given text document like a book/chapter/essay.
In this tutorial, we examine the new YOLOv7 & its new features, learn how to prepare custom datasets for the model, and then build a YOLOv7 model from scratch using NBA footage to detect and discern the ball handler from players on the court.
This blog details different techniques for filtering image data and explores what these filters actually do to an image as it passes through the layers of a Convolutional Neural Network (CNN).
In our newest tutorial, we examine how we can leverage mixed precision training in our Deep Learning code, what frameworks support mixed precision training, and performance tips on using GPUs for Deep Learning with automatic mixed precision.
This deep learning tutorial overview covers mixed precision training, the hardware required to take advantage of such computational capability, and the advantages of using mixed precision training in detail.
This introductory tutorial covers what GANs really are, shows how they work, dives deep into the loss function that they use, and then shows us how to build our first simple GAN from scratch to generate MNIST.
In our latest blogpost, we shine a spotlight on the Nvidia A100 to take a technical examination of the technology behind them, their components, architecture, and how the innovations within have made them the best tool for deep learning.
It's easy to forget that these systems have so many players in place outside of the technical roles, but they're the ones that keep the ship moving. I will be curious if this has an at all stagnating affect, but this is Google, so it is doubtful.
I've been following these fellows for a bit, and haven't seen them mentioned here. This is a really fascinating product. What do data scientists and ML engineers think of this sort of tech breaking into the market?