This is an open, unconventional textbook covering mathematics, computing, and artificial intelligence from foundational principles. It's designed for practitioners seeking a deep understanding, moving beyond exam preparation and focusing on real-world application. The author, drawing from years of experience in AI/ML, has compiled notes that prioritize intuition, context, and clear explanations, avoiding dense notation and outdated material.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
The compendium covers a broad range of topics, from vectors and matrices to machine learning, computer vision, and multimodal learning, with future chapters planned for areas like data structures and AI inference.
NVIDIA GTC is the premier AI conference and exhibition. Learn about the latest advancements in AI, deep learning, and accelerated computing. Includes keynote speakers, sessions, workshops, and an exhibit hall.
3D simulations and movement control with PyBullet. This article demonstrates how to build a 3D environment with PyBullet for manually controlling a robotic arm, covering setup, robot loading, movement control (position, velocity, force), and interaction with objects.
A unified memory stack that functions as a memristor as well as a ferroelectric capacitor is reported, enabling both energy-efficient inference and learning at the edge.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
This blog post details the training of 'Chess Llama', a small Llama model designed to play chess. It covers the inspiration behind the project (Chess GPT), the dataset used (Lichess Elite database), the training process using Huggingface Transformers, and the model's performance (Elo rating of 1350-1400). It also includes links to try the model and view the source code.
This article discusses the history of AI, the split between neural networks and symbolic AI, and the recent vindication of neurosymbolic AI through the advancements of models like o3 and Grok 4. It argues that combining the strengths of both approaches is crucial for achieving true AI and highlights the resistance to neurosymbolic AI from some leaders in the deep learning field.
