Category: neural-networks
-
Optimizing Deep Learning Models with SAM
Optimizing Deep Learning Models with SAM A deep dive into the Sharpness-Aware-Minimization (SAM) algorithm and how it improves the generalizability of modern deep learning models The post Optimizing Deep Learning Models with SAM appeared first on Towards Data Science. Anindya Dey Go to original source
-
On the Possibility of Small Networks for Physics-Informed Learning
On the Possibility of Small Networks for Physics-Informed Learning A new kind of hyperparameter study The post On the Possibility of Small Networks for Physics-Informed Learning appeared first on Towards Data Science. Conor Rowan Go to original source
-
How Convolutional Neural Networks Learn Musical Similarity
How Convolutional Neural Networks Learn Musical Similarity Learning audio embeddings with contrastive learning and deploying them in a real music recommendation app The post How Convolutional Neural Networks Learn Musical Similarity appeared first on Towards Data Science. Luke Stuckey Go to original source
-
AI Papers to Read in 2025
AI Papers to Read in 2025 Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science. The post AI Papers to Read in 2025 appeared first on Towards Data Science. Ygor Serpa Go to original source
-
Physics-Informed Neural Networks for Inverse PDE Problems
Physics-Informed Neural Networks for Inverse PDE Problems Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Marco Hening Tallarico Go to original source
-
Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners
Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners Staying on top of a fast-growing research field is never easy. I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). New papers, be they algorithmic advancements or cutting-edge applications, are published at an accelerating pace by both academia and…
-
Formulation of Feature Circuits with Sparse Autoencoders in LLM
Formulation of Feature Circuits with Sparse Autoencoders in LLM Large Language models (LLMs) have witnessed impressive progress and these large models can do a variety of tasks, from generating human-like text to answering questions. However, understanding how these models work still remains challenging, especially due a phenomenon called superposition where features are mixed into one…
-
Neural Networks – Intuitively and Exhaustively Explained
Neural Networks – Intuitively and Exhaustively Explained An in-depth exploration of the most fundamental architecture in modern AI “The Thinking Part” by Daniel Warfield using MidJourney. All images by the author unless otherwise specified. Article originally made available on Intuitively and Exhaustively Explained. In this article we’ll form a thorough understanding of the neural network,…
-
Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data
Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data How much data does AI really need? TLDR: Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST¹ to classify handwritten digits. Best runs for “furthest-from-centroid” selection compared to full dataset. Image by author. What if I told you…
-
Your Neural Network Can’t Explain This. TMLE to the Rescue!
Your Neural Network Can’t Explain This. TMLE to the Rescue! Targeted Maximum Likelihood Estimation (TMLE) helps you explain patterns where other techniques fall short Continue reading on Towards Data Science » Ari Joury, PhD Go to original source
-
Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems
Understanding Emergent Capabilities in LLMs: Lessons from Biological Systems How natural systems fundamental laws help explain AI’s unexpected abilities Continue reading on Towards Data Science » Javier Marin Go to original source
-
Large Language Models: A Short Introduction
Large Language Models: A Short Introduction And why you should care about LLMs Image by author. There’s an acronym you’ve probably heard non-stop for the past few years: LLM, which stands for Large Language Model. In this article we’re going to take a brief look at what LLMs are, why they’re an extremely exciting piece of technology, why…
-
A Visual Understanding of Neural Networks
A Visual Understanding of Neural Networks The math behind neural networks visually explained Continue reading on Towards Data Science » Reza Bagheri Go to original source
-
How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research
How Recurrent Neural Networks (RNNs) Are Revolutionizing Decision-Making Research A deep dive into the world of computational modeling and its applications Continue reading on Towards Data Science » Kaushik Rajan Go to original source
-
Superposition: What Makes it Difficult to Explain Neural Network
Superposition: What Makes it Difficult to Explain Neural Network When there are more features than model dimensions Introduction It would be ideal if the world of neural network represented a one-to-one relationship: each neuron activates on one and only one feature. In such a world, interpreting the model would be straightforward: this neuron fires for…
-
Introduction to TensorFlow’s Functional API
Introduction to TensorFlow’s Functional API Learn what the Functional API is, and how to build complex keras models using it Continue reading on Towards Data Science » Javier Martínez Ojeda Go to original source
-
Efficient Large Dimensional Self-Organising Maps with PyTorch
Efficient Large Dimensional Self-Organising Maps with PyTorch Because it’s fun to self-organise Continue reading on Towards Data Science » Mathieu d’Aquin Go to original source