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TensorFlow for Computer Vision – Transfer Learning Made Easy
Writing neural network model architectures from scratch involves a lot of guesswork. How many layers? How many nodes per layer? What activation function to use? Regularization? You won’t run out of questions any time soon. Transfer learning takes a different approach. Instead of starting from scratch,... Read more
Fast, Visual, and Explainable ML Modeling With PerceptiLabs
Pure-code ML frameworks like TensorFlow, have become popular for building ML models because they effectively offer a high-level grammar for describing model topologies and algorithms. This is a powerful approach, but it has limitations for providing insight and explainability of models. These issues are further magnified... Read more
Deep Learning-Driven Text Summarization & Explainability with Reuters News Data
Editor’s note: At ODSC West 2020, Nadja Herger, Nina Hristozova, and Viktoriia Samatova will hold a workshop focused on text summarization and that will allow you to automatically generate news headlines powered by Reuters News, and learn about the power of transfer learning and explainable AI.... Read more
Watch: Effective Transfer Learning for NLP
Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to... Read more
Techniques to Overcome Data Scarcity
Editor’s note: Attend ODSC East 2019 this April 30 to May 3 in Boston and check out Parinaz’ talk, “Data Efficiency Through Transfer Learning” there! Supervised machine learning models are being used to successfully solve a whole range of business challenges. However, these models are data-hungry and... Read more