Imagine you trained a machine learning model. Maybe, a couple of candidates to choose from.You ran them on the test set and got some quality estimates. Models are not overfitted. Features make sense. Overall, they perform as well as they can, given the limited data at hand.Now, it is time to decide if any of them is good enough for production use. How to evaluate … [Read more...] about What Is Your Model Hiding? A Tutorial on Evaluating ML Models
Technical Guide
BERT Inner Workings
I created this notebook to better understand the inner workings of Bert. I followed a lot of tutorials to try to understand the architecture, but I was never able to really understand what was happening under the hood. For me it always helps to see the actual code instead of just simple abstract diagrams that a lot of times don’t match the actual implementation. If you’re like … [Read more...] about BERT Inner Workings
GPT2 For Text Classification Using Hugging Face Transformers
This notebook is used to fine-tune GPT2 model for text classification using Hugging Face transformers library on a custom dataset.Hugging Face is very nice to us to include all the functionality needed for GPT2 to be used in classification tasks. Thank you Hugging Face!I wasn’t able to find much information on how to use GPT2 for classification so I … [Read more...] about GPT2 For Text Classification Using Hugging Face Transformers
A Comprehensive Introduction to Bayesian Deep Learning
Photo by Cody Hiscox on UnsplashPreamble Neural Network Generalization Back to Basics: The Bayesian Approach Frequentists Bayesianists Bayesian Inference and Marginalization How to Use a Posterior in Practice? Maximum A Posteriori Estimation Full Predictive Distribution Approximate Predictive Distribution Bayesian Deep Learning Recent Approaches to Bayesian Deep … [Read more...] about A Comprehensive Introduction to Bayesian Deep Learning
Extractive Text Summarization Using Contextual Embeddings
Text Summarization is a process of generating a compact and meaningful synopsis from a huge volume of text. Sources for such text include news articles, blogs, social media posts, all kinds of documentation, and many more. If you are new to NLP and want to read more about text summarization, this article will help you understand the basic and advanced concepts. The … [Read more...] about Extractive Text Summarization Using Contextual Embeddings
Graph Neural Networks for Multi-Relational Data
This article describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encode the structure of multi-relational data, such as Knowledge Graphs (KGs).The article includes 4 main sections:an introduction to the key idea of multi-relational data, which describes the peculiarity of KGs;a summary of the standard components included in a GNN … [Read more...] about Graph Neural Networks for Multi-Relational Data
Graph Attention Networks Under the Hood
Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. GNNs are able to drive improvements for high-impact problems in different fields, such as content recommendation or drug discovery. Unlike other types of data such as images, learning from graph data requires specific methods. As defined by Michael Bronstein:[..] these methods … [Read more...] about Graph Attention Networks Under the Hood
Graph Transformer: A Generalization of Transformers to Graphs
This blog is based on the paper A Generalization of Transformer Networks to Graphs with Xavier Bresson at 2021 AAAI Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI’21).We present Graph Transformer, a transformer neural network that can operate on arbitrary graphs.BLOG OUTLINE:BackgroundObjectiveKey Design Aspects for … [Read more...] about Graph Transformer: A Generalization of Transformers to Graphs
Autoencoders: Overview of Research and Applications
Since the early days of machine learning, it has been attempted to learn good representations of data in an unsupervised manner. The hypothesis underlying this effort is that disentangled representations translate well to downstream supervised tasks. For example, if a human is told that a Tesla is a car and he has a good representation of what a car looks like, he can probably … [Read more...] about Autoencoders: Overview of Research and Applications
Variational Methods in Deep Learning
Deep neural networks are a flexible family of models wide applications in AI and other fields. Even though these networks often encompass millions or even billions of parameters, it is still possible to train them effectively using the maximum likelihood principle as well as stochastic gradient descent techniques. Unfortunately, this learning procedure only gives us a … [Read more...] about Variational Methods in Deep Learning