In this article, we examine the processes of implementing training, undergoing validation, and obtaining accuracy metrics - theoretically explained at a high level. We then demonstrate them by combining all three processes in a class, and using them to train a convolutional neural network.
In this article, we explore what dimensions imply in a convolutional neural network context. We show how to create a custom convnet as a baseline model, and then proceed to create new versions of it, with increased width, increased depth and the last one with both increased depth and width.
In this article, we explain the SVM algorithm generally, and then show how to use the LIBSVM package in a code demo. After the code section, we will share some additional tips to help improve the performance of our model, as well as some assumptions and limitations of the algorithm.
We spoke with an educator who uses Paperspace to teach students and business professionals about machine learning and AI.
We couldn't pass up the chance to talk to Rohan Tondulkar, a Senior Research Scientist at SciSpace using machine learning to make scientific literature more approachable and collaboration more accessible.
We spoke with Alex Castrounis from the 1871 innovation hub in Chicago to learn more about the intersection of academic and enterprise-focused machine learning instruction.
