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 .
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 .
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 .
A step-by-step journey from calculus-based optimization to Stochastic Gradient Descent The post Why Gradient Descent Became Stochastic appeared first on Towards Data Science .
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.
The article titled "10 Things I’m Learning Beyond AI to Become More Technologically Fluent" discusses the author's exploration of various technologies that are shaping the future, beyond just artificial intelligence. It is presented as part one of a series, focusing on understanding these emerging technologies and their impact.
Backpropagation is the computational engine of deep learning, yet its mathematical structure is typically treated as a procedural traversal of computational graphs. We present a global operator theory of the \emph{F-adjoint} framework, which reformulates the layerwise backward recursion of an $L$-depth feedforward network into a single linear system $(I-\cB)\Xs=\bG$, where $\bG$ is a source vector.
The article "How to Work with AI Coding Agents" offers a practical guide aimed at improving code quality rather than merely increasing quantity. It focuses on strategies and best practices for effectively collaborating with AI coding tools to produce better code. The post was originally published on Towards Data Science.
The downside of conference travel The post Last Month’s Machine Learning Lessons Learned appeared first on Towards Data Science .
arXiv:2607. 11289v1 Announce Type: cross Abstract: Backpropagation is the computational engine of deep learning, yet its mathematical structure is typically treated as a procedural traversal of computational graphs.
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.
A clear, math-first walkthrough of how VAEs learn to generate new data The post Variational Autoencoders (VAEs) Explained: From Theory to ELBO and the Reparameterization Trick appeared first on Towards Data Science .