How to Train Your Robot
The enormous advance in AI capabilities over the last several years has in large part been driven by the availability of vast amounts of training data.
deep-learning
The AI Inference Revolution Is Here
Today’s tidal wave of queries is forcing hardware makers to pivot
Note: ### The AI Inference Revolution Is Here [IEEE Spectrum article](#rid:1854764141) — a survey of how the shift from AI training to AI **inference** is reshaping chip and memory design. **The shift** Since 2020, the AI story was about training ever-bigger models. In 2026 the spotlight has moved to inference — putting trained models to work. People actually use LLMs now, reasoning models run…
GPT-6 Astra and the Fourth Exponential
AI computer tool use is a big deal.
Beyond the solver: How Geometric Deep Learning is reshaping CAE - Simcenter
Discover how Geometric Deep Learning and Graph Neural Networks power Simcenter PhysicsAI to cut CFD & FEA simulation time from days to seconds.
AI Learns the “Dark Art” of RFIC Design
Freed from intelligibility and aesthetics, AI designs faster
The Secret AI Arms Race That Should Worry Investors
A University of Chicago study changed how I think about AI and information edges. Then a second paper on AI made it worse.
CUDA for Deep Learning - Elliot Arledge
CUDA (Compute Unified Device Architecture) provides a powerful parallel programming model AI engineers can use to tap the massive processing power of NVIDIA GPUs. CUDA delivers direct control, debugging power, and acceleration at the GPU level that can’t be matched by other…
The thing that makes 4-bit AI work is a grid of nothing but +1s and -1s called a Hadamard matrix. Here's how a 159 year old matrix ended up…
The thing that makes 4-bit AI work is a grid of nothing but +1s and -1s called a Hadamard matrix. Here's how a 159 year old matrix ended up rescuing modern AI model performance: In 1867 James Joseph Sylvester was studying how to color tiles. He found you could build square grids…
An Overview of AI Accelerators: MAC Operations, DRAM/SRAM, Performance Metrics, and Architecture
Learning from a classic AI accelerator - Eyeriss
Sparse AI Hardware Slashes Energy and Latency
Sparse computing enables leaner, faster AI
Sigmoid vs ReLU Activation Functions: The Inference Cost of Losing Geometric Context - MarkTechPost
Explore Sigmoid vs ReLU activation functions and how losing geometric context affects neural network efficiency and depth.
Autonomous Driving: Assessment Of YOLO Algorithms (RMIT et al.)
A new technical paper titled “Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles” was published by RMIT University, Kyungpook National University, Deakin University and the RCA Robotics Laboratory, Royal College of Art. Abstract…
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
The last decade has witnessed an experimental revolution in data science and machine learning, epitomised by deep learning methods. Indeed, many high-dimensional learning tasks previously thought to be beyond reach -- such as computer vision, playing Go, or protein folding…
What Is AI Red Teaming? Top 18 AI Red Teaming Tools (2025)
Discover top AI red teaming tools for robust AI security. Learn how adversarial testing protects machine learning models
Deep Learning with Python, Second Edition by François Chollet - Free PDF
A Gentle Introduction to Graph Neural Networks
What components are needed for building learning algorithms that leverage the structure and properties of graphs?
BERT
BERT is an open source machine learning framework for natural language processing (NLP) that helps computers understand ambiguous language by using context
Why it’s so hard to use AI to diagnose cancer
The latest effort, from the Mayo Clinic, holds some clues.
5 AI Image Generators You Can Use Now
AI art generation has entered an era of evolution over revolution
The 2025 AI Engineering Reading List
We picked 50 paper/models/blogs across 10 fields in AI Eng: LLMs, Benchmarks, Prompting, RAG, Agents, CodeGen, Vision, Voice, Diffusion, Finetuning. If you're starting from scratch, start here.
Note: gist: https://gist.github.com/bjpcjp/117ecf144c062c3c933f2690e7ece5c2
Lessons from Optics, The Other Deep Learning
Musings on systems, information, learning, and optimization.
Choosing and Implementing Hugging Face Models
Pulling pre-trained models out of the box for your use case
5 Free Books on Computer Vision - MachineLearningMastery.com
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Aman's AI Journal • Primers • Ilya Sutskever's Top 30
Aman's AI Journal | Course notes and learning material for Artificial Intelligence and Deep Learning Stanford classes.
Summary of Ilya Sutskevers AI Reading List · Tensor Labbet
Note: CNNs (5 items) RNNs (10 items) Transformers (3 items) Info theory (5 items) Other (4 items)
Dario Amodei — Machines of Loving Grace
How AI Could Transform the World for the Better
10 GitHub Repositories for Advanced Machine Learning Projects
Where can you find projects dealing with advanced ML topics? GitHub is a perfect source with its many repositories. I’ve selected ten to talk about in this article.
Top Books on Deep Learning and Neural Networks
Deep learning is crucial in today's age as it powers advancements in artificial intelligence, enabling applications like image and speech recognition, language translation, and autonomous vehicles. Understanding deep learning equips individuals to harness its potential, driving…
Kolmogorov-Arnold Networks (KANs): A New Era of Interpretability and Accura
Multi-layer perceptrons (MLPs), or fully-connected feedforward neural networks, are fundamental in deep learning, serving as default models for approximating nonlinear functions. Despite their importance affirmed by the universal approximation theorem, they possess drawbacks. In…
Deep Learning Architectures From CNN, RNN, GAN, and Transformers To Encoder
Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models. This article…
Mamba Explained
Is Attention all you need? Mamba, a novel AI model based on State Space Models (SSMs), emerges as a formidable alternative to the widely used Transformer models, addressing their inefficiency in processing long sequences.
[2304.05055] A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied...
Conformal_Prediction/paper/Conformal_Prediction_final.pdf at main · MichelL
This projects contains different conformal methods and approaches. Includes code generated for a experimental evaluation of a multidimensional, low-sample size biomedical dataset of oncological sub...
The Math behind Adam Optimizer | by Cristian Leo | in Towards Data Science
Why is Adam the most popular optimizer in Deep Learning? Let's understand it by diving into...
Thinking about High-Quality Human Data | Lil'Log
[Special thank you to Ian Kivlichan for many useful pointers (E.g. the 100+ year old Nature paper “Vox populi”) and nice feedback. 🙏 ] High-quality data is the fuel for modern data deep learning model training. Most of the task-specific labeled data comes from human annotation…
Give AI curiosity, and it will watch TV forever
TV is just as interesting to AI as it is to humans.
Meet neograd: A Deep Learning Framework Created from Scratch Using Python a
Understanding how convolutional neural networks (CNNs) operate is essential in deep learning. However, implementing these networks, especially convolutions and gradient calculations, can be challenging. Many popular frameworks like TensorFlow and PyTorch exist, but their complex…
Understanding and Coding Self-Attention, Multi-Head Attention, Cross-Attent
This article will teach you about self-attention mechanisms used in transformer architectures and large language models (LLMs) such as GPT-4 and Llama.
Understanding Deep Learning
Variational Autoencoder (VAE) with Discrete Distribution using Gumbel Softm
Theory and PyTorch Implementation
Calculate Computational Efficiency of Deep Learning Models with FLOPs and M
In this article we will learn about its definition, differences and how to calculate FLOPs and MACs using Python packages.
ELI5: FlashAttention
Step by step explanation of how one of the most important MLSys breakthroughs work — in gory detail.
Why You Need To Know About Autonomous AI Agents
A beginner's guide to understanding autonomous AI agents and their importance.
The Case for Running AI on CPUs Isn’t Dead Yet
GPUs may dominate, but CPUs could be perfect for smaller AI models
Photonic Chips Curb AI Training’s Energy Appetite
Stanford team achieves first-ever optical backpropagation milestone
A Survey of Large Language Models
Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and...
New Ebook: A Beginner’s Guide to Large Language Models
Explore what LLMs are, how they work, and gain insights into real-world examples, use cases, and best practices.
The Sequence Chat: Salesforce Research's Junnan Li on Multimodal Generative AI
One of the creators of the famous BLIP-2 model shares his insights about the current state of multimodal generative AI.
📝 Guest Post: Caching LLM Queries for Improved Performance and Cost Savings*
If you're looking for a way to improve the performance of your large language model (LLM) application while reducing costs, consider utilizing a semantic cache to store LLM responses.
How to use Midjourney to generate AI images | Digital Trends
AI-generated images have never been more popular, and Midjourney is one of the best tools. Here's how to access the AI and what to know about using it.