Understanding math will make you a better engineer.So, I am writing the best and most comprehensive book about it. I'm interested Knowing the mathematics behind machine learning algorithms is a superpower. If you have ever built a model for a real-life problem, you probably experienced that familiarity with the details goes a long way if you want to move beyond baseline performance. This is especi
DeepSeek has recently released DeepSeek v3, which is currently state-of-the-art in benchmark performance among open-weight models, alongside a technical report describing in some detail the training of the model. Impressively, they’ve achieved this SOTA performance by only using 2.8 million H800 hours of training hardware time—equivalent to about 4e24 FLOP if we assume 40% MFU. This is about ten t
Patterns for Building LLM-based Systems & Products [ llm engineering production 🔥 ] · 66 min read Discussions on HackerNews, Twitter, and LinkedIn “There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.”
How can neural networks learn the rich internal representations required for difficult tasks such as recognizing objects or understanding language? Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have made deep neural networks a critical component of computing. Research on artificial neural networks was motivated by the observa
Migrating Critical Traffic At Scale with No Downtime — Part 2
Shyam Gala, Javier Fernandez-Ivern, Anup Rokkam Pratap, Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. Behind these perfect moments of entertainment is a complex mechanism, with numerous gears and cogs working in harmony. But what happens when this machinery needs a transformation?
Ilya Sutskever’s Top 30 Reading List The First Law of Complexodynamics The Unreasonable Effectiveness of Recurrent Neural Networks Understanding LSTM Networks Recurrent Neural Network Regularization Keeping Neural Networks Simple by Minimizing the Description Length of the Weights Pointer Networks ImageNet Classification with Deep Convolutional Neural Networks Order Matters: Sequence to Sequence f
The Principles of Deep Learning Theory An Effective Theory Approach to Understanding Neural Networks Daniel A. Roberts and Sho Yaida based on research in collaboration with Boris Hanin drob@mit.edu, shoyaida@fb.com ii Contents Preface vii 0 Initialization 1 0.1 An Effective Theory Approach . . . . . . . . . . . . . . . . . . . . . . . . 2 0.2 The Theoretical Minimum . . . . . . . . . . . . . . . .
This article appeared on Hacker News. Link to the discussion here. Additionally, Andrej Karpathy wrote his thoughts about the post, linked here. A while back, Google DeepMind unveiled Gemini Diffusion, an experimental language model that generates text using diffusion. Unlike traditional GPT-style models that generate one word at a time, Gemini Diffusion creates whole blocks of text by refining ra
Why We Think
Date: May 1, 2025 | Estimated Reading Time: 40 min | Author: Lilian Weng Special thanks to John Schulman for a lot of super valuable feedback and direct edits on this post. Test time compute (Graves et al. 2016, Ling, et al. 2017, Cobbe et al. 2021) and Chain-of-thought (CoT) (Wei et al. 2022, Nye et al. 2021), have led to significant improvements in model performance, while raising many research
111 A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT YIHAN CAO∗, Lehigh University & Carnegie Mellon University, USA SIYU LI, Lehigh University, USA YIXIN LIU, Lehigh University, USA ZHILING YAN, Lehigh University, USA YUTONG DAI, Lehigh University, USA PHILIP S. YU, University of Illinois at Chicago, USA LICHAO SUN, Lehigh University, USA Recen
The Little Book of Deep Learning
The Little Book of Deep Learning François Fleuret François Fleuret is a professor of computer sci- ence at the University of Geneva, Switzerland. The cover illustration is a schematic of the Neocognitron by Fukushima [1980], a key an- cestor of deep neural networks. This ebook is formatted to fit on a phone screen. Contents Contents 5 List of figures 7 Foreword 8 I Foundations 10 1 Machine Learnin
This article will discuss building a simple neural network using JavaScript. However, let’s first check what deep neural networks and artificial neural networks are. Deep Neural Network and Artificial Neural NetworkArtificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) are related concepts, but they are different. The inspiration behind these artificial neural networks for machine learn
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
The Roadmap of Mathematics for Machine Learning
How has DeepSeek improved the Transformer architecture?
Patterns for Building LLM-based Systems & Products
Deep Learning for AI – Communications of the ACM
Aman's AI Journal • Primers • Ilya Sutskever's Top 30
https://deeplearningtheory.com/PDLT.pdf
BERT is just a Single Text Diffusion Step
A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
Building a Simple Artificial Neural Network in JavaScript