From Machine Learning Engineering in Action by Ben Wilson
Before we get into how successful planning phases for ML projects are undertaken, let’s go through a simulation of the genesis of a typical project at a company that doesn’t have an established or proven process for initiating ML work.
From Transfer Learning in Action Dipanjan Sarkar and Raghav Bali
This article delves into tuning up a pre-trained ResNet-50 with one-cycle learning rate.
From Graph-Powered Machine Learning by Alessandro Negro
This article discusses managing data in graph-powered machine learning projects.
By Graph-Powered Machine Learning Alessandro Negro
This article discusses creating a bigraph for a user-item dataset.
This article talks about the need to carefully plan a machine learning project—before you start it!
From Transfer Learning for Natural Language Processing by Paul Azunre
In this article, we cover some representative deep transfer learning modeling architectures for NLP that rely on a recently popularized neural architecture – the transformer – for key functions.
In this video, Hobson shows you how to move words from inflammatory to less inflammatory context with the help of word vectors (Word2vec).
Hefin I. Rhys teaches you how to train, tune, and cross-validate a Support Vector Machine model using RStudio and the awesome mlr machine learning package.
Learn the most important tools in the repertoire of a data scientist and machine learning practitioner – Principal Component Analysis (PCA), Singular Value Decomposition (SVD), and Latent Semantic Analysis (LSA) – with the help of Krishnendu Chaudhury.
Deep dive with Carl Osipov into understanding automatic differentiation used by PyTorch autograd for deep learning