In the third part of this six-part series, we will use the TensorFlow Recommenders library to build a basic recommender model and train it on the data we prepared in Part 2.
For many organizations, a major question faced by data scientists and engineers is how best to go from the experimentation stage to production. In the second part of a six-part series, we'll show how to go from a raw dataset to a suitable model training set using TensorFlow 2 and Gradient notebooks.
For many organizations, a major question faced by data scientists and engineers is how best to go from the experimentation stage to production. In this first part of six, we outline the most important part of any enterprise data science analysis – posing a business problem.
Deep learning techniques receive much of the attention lately but classical ML techniques like GBTs are sometimes faster and easier to implement. Here we'll take a look at training a model with GBTs and then we'll deploy that model to an endpoint!
In the second part of a series on audio analysis and processing, we'll look at notes, harmonics, octaves, chroma representation, onset detection methods, beat, tempo, tempograms, spectrogram decomposition, and more!
Paperspace contributor Nigama Vykari guides us through use of the Hough transform feature extraction technique in the context of lane detection for self-driving cars.
ATG Fellowships are back! If you're a Graduate or Post-Grad looking to continue deep learning research with the support of a talented engineering team, join us for a 10-15 week fully funded research appointment!
In this overview of MLOps, Paperspace contributor Joydip Kanijilal covers the basics of MLOps and building resilient machine learning applications.
We're pleased to release a new ML Showcase entry featuring some exciting new GPU-accelerated visualization techniques courtesy of NVIDIA RAPIDS.
