What We Learned from a Year of Building with LLMs (Part I)
It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into
Solving Quantitative Reasoning Problems With Language Models
Solving Quantitative Reasoning Problems with Language Models Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†, Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur∗, Guy Gur-Ari∗, and Vedant Misra∗ Google Research Abstract Language models have achieved remarkable performance on a wide range of tasks that require
What We’ve Learned From A Year of Building with LLMs – Applied LLMs
A practical guide to building successful LLM products, covering the tactical, operational, and strategic. It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. And they’re getting better and cheaper every year. Coupled with a parade of demos on social media, there will be an estimated $200B investment in AI
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 . . . . . . . . . . . . . . . .
Large Text Compression Benchmark Matt Mahoney Last update: Mar. 25, 2026. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compress
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
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
Aman's AI Journal • Primers • Ilya Sutskever's Top 30
https://deeplearningtheory.com/PDLT.pdf
Large Text Compression Benchmark