The intuition behind neural networks and why they need activation functions. The post Neural Networks, Explained for Beginners: Start Here If They’ve Confused You appeared first on Towards Data Science .
Let's discover how neural networks learn, step by step The post Backpropagation Explained for Beginners (Part 1): Building the Intuition appeared first on Towards Data Science .
From one gradient to every gradient The post Backpropagation Explained for Beginners (Part 3): How Backpropagation Really Works appeared first on Towards Data Science .
arXiv:2507. 14177v2 Announce Type: replace-cross Abstract: This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose hidden layer is composed of the units with smooth activation functions, including the usual sigmoid type most commonly used before the advent of ReLUs.
arXiv:2006. 04013v6 Announce Type: cross Abstract: Artificial Intelligence (AI) has been adopted in a wide range of domains.
The article titled "The Sigmoid Function: From 'e' to Neural Networks" explores the origins and applications of the sigmoid function, a mathematical equation frequently used in data science and machine learning. It traces the function’s development from its foundational exponential form to its modern role in neural network architectures. The piece highlights how this simple yet powerful equation underpins many computational models in the field.
We’ve created activation atlases (in collaboration with Google researchers), a new technique for visualizing what interactions between neurons can represent. As AI systems are deployed in increasingly sensitive contexts, having a better understanding of their internal decision-making processes will let us identify weaknesses and investigate failures.
arXiv:2606. 06624v1 Announce Type: new Abstract: In the current era of deep learning and especially generative models, there is significant investment in training very large generative models.
The idea that makes backpropagation possible. The post Backpropagation Explained for Beginners (Part 2): There Has to Be a Better Way appeared first on Towards Data Science .
arXiv:1906. 09235v3 Announce Type: replace Abstract: Along with fruitful applications of Deep Neural Networks (DNNs) to realistic problems, recently, some empirical studies of DNNs reported a universal phenomenon of Frequency Principle (F-Principle): a DNN tends to learn a target function from low to high frequencies during the training.
The paper introduces an algebraic framework that links neural networks to neural ideals, providing algorithms for computing and approximating these ideals. It demonstrates how to identify and interpret the features captured by each hidden‑layer neuron, validated on the MNIST dataset. An interactive software tool is released to visualize these neuron‑specific features.
The article "Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks" discusses how Bayesian neural networks enable more informed decision-making by quantifying uncertainty in predictions. It introduces practical aspects of implementing these models and highlights their advantages over traditional point prediction approaches.