Intro to optimization in deep learning: Gradient Descent
An in-depth explanation of Gradient Descent and how to avoid the problems of local minima and saddle points.
Intro to optimization in deep learning: Momentum, RMSProp and Adam
In this post, we take a look at a problem that plagues training of neural networks, pathological curvature.
Model Interpretability and Understanding for PyTorch using Captum
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.
Boosting Python Scripts With Cython
Python might be one of today’s most popular languages, but it’s definitely not the most efficient. See how Cython can easily boost your Python scripts.
Optimization-based meta-learning: Using MAML with PyTorch on the MNIST dataset
In this tutorial, we continue looking at MAML optimization methods with the MNIST dataset.
Introduction to Extractive and Abstractive Summarization Techniques
In this theory we cover the background theory behind a variety of methodologies for abstractive text summarization
Automatic Mixed Precision Using PyTorch
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.
