Transformer Architecture: The Positional Encoding - Amirhossein Kazemnejad's Blog

kazemnejad.com/blog/transformer_architecture_positional_encoding/

Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.

yaofu.notion.site/Towards-100x-Speedup-Full-Stack-Transformer-Inference-Optimization-43124c3688e14cffaf2f1d6cbdf26c6c

arxiv.org/pdf/2403.19887v1

Musings on Building a Generative AI Product

www.linkedin.com/blog/engineering/generative-ai/musings-on-building-a-generative-ai-product

2404.14619v1.pdf

arxiv.org/pdf/2404.14619

2404.02258.pdf

arxiv.org/pdf/2404.02258.pdf

Answer.AI - A few tips for working on high-surface-area problems

www.answer.ai/posts/2024-04-12-tips.html

2203.11171.pdf

arxiv.org/pdf/2203.11171.pdf

2404.07965.pdf

arxiv.org/pdf/2404.07965.pdf

2403.07815.pdf

arxiv.org/pdf/2403.07815.pdf

2403.17297.pdf

arxiv.org/pdf/2403.17297.pdf

2403.08763.pdf

arxiv.org/pdf/2403.08763.pdf

2403.13372.pdf

arxiv.org/pdf/2403.13372.pdf

Keras documentation: Video Classification with a CNN-RNN Architecture

keras.io/examples/vision/video_classification/

Adapting language model architectures for time series forecasting

www.amazon.science/blog/adapting-language-model-architectures-for-time-series-forecasting

Tuning GPT-3 on a Single GPU

decentdescent.org/tp5.html

Apple One

www.apple.com/in/apple-one/

Notion – The all-in-one workspace for your notes, tasks, wikis, and databases.

yaofu.notion.site/How-Do-Language-Models-put-Attention-Weights-over-Long-Context-10250219d5ce42e8b465087c383a034e

2403.04652.pdf

arxiv.org/pdf/2403.04652.pdf

Answer.AI - You can now train a 70b language model at home

www.answer.ai/posts/2024-03-06-fsdp-qlora.html?utm_source=ainews&utm_medium=email&utm_campaign=ainews-fsdpqlora-the-answer-to-70b-scale-ai-for

2401.13697v1.pdf

arxiv.org/pdf/2401.13697v1.pdf

Mamba: The Easy Way

jackcook.com/2024/02/23/mamba.html

2306.15796.pdf

arxiv.org/pdf/2306.15796.pdf

txyz.ai - Integrate all paths to knowledge

app.txyz.ai/chat/5d94b7c4-3943-4929-b4f1-e85a60fcee68

(5) Transformers demystified: how do ChatGPT, GPT-4, LLaMa work? - YouTube

www.youtube.com/watch?v=C6ZszXYPDDw

chinchilla's wild implications — AI Alignment Forum

www.alignmentforum.org/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications

Generative AI Data Infrastructure: How to Train Large Language Models (LLMs) with Deep Lake

www.activeloop.ai/resources/generative-ai-data-infrastructure-how-to-train-large-language-models-ll-ms-with-deep-lake/

2311.05845.pdf

arxiv.org/pdf/2311.05845.pdf

At the Intersection of LLMs and Kernels - Research Roundup

charlesfrye.github.io/programming/2023/11/10/llms-systems.html

Windows 11 version 23H2: All the new features in Microsoft's big OS update

www.windowscentral.com/software-apps/windows-11/windows-11-version-23h2-new-features-release-date-changelog-2023-update

Introducing the Inko website and manual

inko-lang.org/news/hello-world/

Keras documentation: Audio Classification with Hugging Face Transformers

keras.io/examples/audio/wav2vec2_audiocls/

Particle Filter Localization with Webots and ROS2

towardsdatascience.com/particle-filter-localization-with-webots-and-ros2-619ecf0c5f08

RedPajama-Data-v2: an Open Dataset with 30 Trillion Tokens for Training Large Language Models — Together AI

together.ai/blog/redpajama-data-v2

Quickly build high-accuracy Generative AI applications on enterprise data using Amazon Kendra, LangChain, and large language models | Amazon Web Services

aws.amazon.com/blogs/machine-learning/quickly-build-high-accuracy-generative-ai-applications-on-enterprise-data-using-amazon-kendra-langchain-and-large-language-models/

Run SQL queries from your SageMaker notebooks using Amazon Athena | Amazon Web Services

aws.amazon.com/blogs/machine-learning/run-sql-queries-from-your-sagemaker-notebooks-using-amazon-athena/

Audio classification architectures - Hugging Face Audio Course

huggingface.co/learn/audio-course/chapter3/classification

Unit 3. Transformer architectures for audio - Hugging Face Audio Course

huggingface.co/learn/audio-course/chapter3/introduction

Understanding sync vs async methods in Fast API

medium.com/towards-data-engineering/understanding-sync-vs-async-methods-in-fast-api-fa4aec9dd233

Building a Geospatial cache in Go

tonyalaribe.medium.com/building-a-geospatial-cache-in-go-ab635777ccb1

Making Deep Learning go Brrrr From First Principles

horace.io/brrr_intro.html

Itamar Golan 🤓 on X

twitter.com/ItakGol/status/1705885984741523821?t=A3D2EpS8xkEyzeMipcXkqA&s=08

Visualizing regularization and the L1 and L2 norms

towardsdatascience.com/visualizing-regularization-and-the-l1-and-l2-norms-d962aa769932

2309.03409.pdf

arxiv.org/pdf/2309.03409.pdf

2307.07924v3.pdf

arxiv.org/pdf/2307.07924v3.pdf

For your eyes only: improving Netflix video quality with neural networks

medium.com/netflix-techblog/for-your-eyes-only-improving-netflix-video-quality-with-neural-networks-5b8d032da09c

2309.01826.pdf

arxiv.org/pdf/2309.01826.pdf

2309.05463.pdf

arxiv.org/pdf/2309.05463.pdf

Comparison of two data fusion methods for localization of wheeled mobile robot in farm conditions

www.sciencedirect.com/science/article/pii/S2589721719300066?ref=pdf_download&fr=RR-2&rr=80570ae82d0646ef

Robot localization with Kalman-Filters and landmarks

jannik-zuern.medium.com/robot-localization-with-kalman-filters-and-landmarks-cf97fa44e80b

1. Introduction — Machine Learing Compilation 0.0.1 documentation

mlc.ai/chapter_introduction/