Optimizing Natural Language Processing Models Using Backtracking Algorithms: A Systematic Approach
Tips for optimizing NLP models with backtracking algorithms, with coded examples.
10 months ago
Adrien Payong
Adrien Payong is a seasoned computer scientist specializing in artificial intelligence research.
Review: Fine-tuned language models are zero-shot learners
In this article, we review several notable fine-tuned language models for their capabilities as zero-shot learners on diverse tasks.
a year ago
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.
a year ago
Unveiling the Power of First-Order MAML Algorithm in Meta-Learning
In this tutorial we introduce and cover First-Order Model Agnostic Meta-Learning (MAML), which give fast understanding on new tasks to deep neural networks.
a year ago
Transformers for Language Translation, Classification and Segmentation challenge
in this tutorial, we look at how Transformers enables several classical NLP techniques like translation, classification, and segmentation of text.
a year ago
GPU Computing for Accelerating Drug Discovery Processes
In this review, we look at how different medical techniques for drug discovery are enabled by Nvidia GPUs like those offered on Paperspace.
a year ago
Using Adapter Transformers at Hugging Face
In this tutorial, we show how to use the HuggingFace AdapterHub to access adapter transformers in Paperspace Notebooks.
a year ago
Graph Neural Networks: Fundamentals, Implementation, and Practical Uses
In this tutorial, we introduce the fundamentals of Graph Neural Networks, and demonstrate how to use them in a Gradient Notebook with Python code to build a custom GNN.
a year ago
Meta Learning for Natural Language - Meta Learning for NLP Tasks: Part 3
Part 3 of our tutorial series on Meta Learning for NLP tasks.
a year ago
