This is a review of the CausalML package, a Python package that provides a suite of uplift modeling and causal inference methods using machine learning algorithms based on recent research.
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
In this article, we take a theoretical lens to the PP-YOLO model, breakdown its model architecture in detail, and compare its features to those of its predecessor YOLO family models.
In this article, we go over Neural Machine Translation with Bahdanau and Luong Attention, and demonstrate the value of the innovative model architecture.
In this tutorial, we show how to apply model interpretability algorithms from Captum on simple models. We demo building a basic model and use attribution algorithms such as Integrated Gradients, Saliency, DeepLift, and NoiseTunnel to attribute the image's label to the input pixels and visualize it.
In this overview of Automatic Mixed Precision (AMP) training with PyTorch, we demonstrate how the technique works, walking step-by-step through the process of integrating AMP in code, and discuss more advanced applications of AMP techniques with code scaffolds to integrate your own code.
In this deep dive of BERT, we explore the powerful NLP model's history, break down the approach and architecture behind the model, and take a look at some relevant experiments. We then close with a code demo showing how to use BERT, DistilBERT, RoBERTa, and ALBERT in a Gradient Notebook.
In this blog post we take an in depth look at the Transformer model architecture, and demo its functionality by rebuilding the model from scratch in Python.
In this blog post, we examine Captum, which supplies academics and developers with cutting-edge techniques, such as Integrated Gradients, that make it simple to identify the elements that contribute to a model's output. We then put these techniques to use in a coding demo with ResNet.
