If you're working with deep neural networks, you know that getting them to converge on a solution can be a challenge. But what if you're trying to train a

0 Skip to Content About Consulting Training Technical Marketing Blog Contact Us Open Menu Close Menu About Consulting Training Technical Marketing Blog Contact Us Open Menu Close Menu About Consulting Training Technical Marketing Blog Contact Us Solutional Blog No results found AI/ML Phil Gervasi 5/14/26 AI/ML Phil Gervasi 5/14/26 Networks Are Graphs, Not Language Problems: A Look at NetAI’s GNN Approach Most AI systems in networking treat operations like a language problem. But networks are fundamentally

**What is overfitting in neural networks?** Overfitting occurs when a neural network learns patterns specific to the tr

Posts tagged with "neural networks" The Lesson in the Solved Corner Published on 2026-07-16 If you're curious about how AI learns to make complex strategic guesses without needing perfect rules, you need to read this. I'm going to walk you through analyzing my self-playing checkers engine, Polonius, to pinpoint exactly where its neural network struggles the most. Read More Different, Not Deeper Published on 2026-05-28 Are you curious about how I pushed my neural network checkers engine past its perceived li

Representation Similarity Reveals Implicit Layer Grouping in Neural Networks for NeurIPS 2025 by Tian Gao et al

top of page Meritocratic.Capital Ventures Knowledge Hub About Tech Blog Careers Tryout Program More Use tab to navigate through the menu items. Alphanome Log In All Posts Search Graph Neural Networks (GNNs) and Causal AI for Investors Aki Kakko Oct 24, 2023 4 min read Updated: Nov 11, 2025 In recent years, the field of deep learning has expanded beyond traditional data types like images and texts, venturing into more complex structures like graphs . This expansion has given birth to Graph Neural Networks (G

blog.skaup.co INSPIRED BY OTHER WRITING Neural Networks and Lisp - Part 1 By: skaup On: Tue 07 October 2025 In: Technical The first few “artificial intelligence” models that we knew of came in the late 50s to early 60s. They were essentially rule based engines. Symbolic computation was involved that allowed for mimicking intelligence. And lisp was the hot language of choice for this work. These systems were non deterministic to a degree, but in all, we were stuffing rules such as “Brad likes apples

Learn about the role of activation functions in neural networks, including the different types of activation functions and how they work

Using Apple’s new BNNS framework to make a basic neural network

Neural networks are machine learning algorithms that are very good at solving tough problems - they’re used for language translation, facial recognition, and financial management. I, however, have been training them on silly datasets. Here are some of my favorite experiments from the last year