deep learning Super-convergence in Tensorflow 2 with the 1Cycle Policy Implementing super-convergence for deep neural network training in Tensorflow 2 with the 1Cycle learning rate policy.
deep learning Finding a Learning Rate with Tensorflow 2 Implementing the technique in Tensorflow 2 is straightforward. Start from a low learning rate, increase the learning rate and record the loss. Stop when a very high learning rate is reached. Plot the losses and learning rates choosing a learning rate where the loss is decreasing at a rapid rate.
fastai African Antelope: A Case Study of Creating an Image Dataset with FastAI An end-to-end example of how to create your own image dataset from scratch and train a ResNet50 convolutional neural network for image classification using the FastAI library.
fastai FastAI Installation and Setup Instructions for installing FastAI v1 within a freshly created Anaconda virtual environment.
fastai CDC Mortality Prediction with FastAI for Tabular Data This post will cover getting started with FastAI v1 at the hand of tabular data. It is aimed at people that are at least somewhat familiar with deep learning, but not necessarily with using the FastAI v1 library.
research How to Read a Paper A few years ago I came across a method for reading academic papers which I've kept coming back to as a reliable systematic approach to efficiently read important papers of varying complexity. The method itself comes from a paper by Prof. Srinivasan Keshav, an ACM Fellow and researcher at the
data science An Overview of LightGBM This post gives an overview of LightGBM and aims to serve as a practical reference. A brief introduction to gradient boosting is given, followed by a look at the LightGBM API and algorithm parameters.
deep learning Encoding Cyclical Features for Deep Learning A key concern when dealing with cyclical features is how we can encode the values such that it is clear to the deep learning algorithm that the features occur in cycles. This post looks at a strategy to encode cyclical features in order to clearly express their cyclical nature.
