Shreyansh Singh
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  • Paper Summary #10 - Gemini 1.5 Pro

    Google DeepMind announced a multimodal LLM with support of up to 10M context length.

    19 min read   ·   February 18, 2024

    2024   ·   llm   multimodal   transformer   ·   LLMs

    Paper Summary #10 - Gemini 1.5 Pro
  • Solving Substitution Ciphers using Markov Chain Monte Carlo (MCMC)

    Deciphering substitution ciphers can be framed as a Markov chain problem and a simple Monte Carlo sampling approach can help solve them very efficiently

    5 min read   ·   July 23, 2023

    2023   ·   sampling   probability   mcmc   cryptography   ·   Mathematics

    Solving Substitution Ciphers using Markov Chain Monte Carlo (MCMC)
  • Paper Summary #9 - Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

    Understanding Sophia - A new fast, scalable second-order optimizer which beats Adam on LLM pretraining.

    31 min read   ·   May 28, 2023

    2023   ·   transformer   optimizer   deep-learning   paper-summaries   ·   Deep Learning   ML Theory

    Paper Summary #9 - Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
  • Paper Summary #8 - FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

    Understanding FlashAttention which is the most efficient exact attention implementation out there, which optimizes for both memory requirements and wall-clock time.

    37 min read   ·   March 26, 2023

    2023   ·   mlsys   transformer   efficiency   attention   paper-summaries   ·   MLSys

    Paper Summary #8 - FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
  • Paper Summary #7 - Efficient Transformers: A Survey

    A survey paper of improvements over the original Transformer architecture in terms of memory-efficiency.

    24 min read   ·   October 10, 2022

    2022   ·   mlsys   transformer   efficiency   attention   paper-summaries   ·   MLSys   LLMs

    Paper Summary #7 - Efficient Transformers: A Survey
  • Deploying Machine Learning models using GCP's Google AI Platform - A Detailed Tutorial

    A step-wise tutorial to demonstrate the steps required to deploy a ML model using GCP, specifically the Google AI Platform and use Streamlit to access the model through a UI.

    21 min read   ·   March 06, 2022

    2022   ·   model-deployment   gcp   streamlit   ·   MLOps

    Deploying Machine Learning models using GCP's Google AI Platform - A Detailed Tutorial
  • Deploying Machine Learning models using AWS Lambda and Github Actions - A Detailed Tutorial

    A step-wise tutorial to demonstrate the steps required to deploy a ML model using AWS Lambda, Github Actions, API Gateway and use Streamlit to access the model API through a UI.

    21 min read   ·   January 23, 2022

    2022   ·   model-deployment   aws   streamlit   github-actions   ·   MLOps

    Deploying Machine Learning models using AWS Lambda and Github Actions - A Detailed Tutorial
  • PPML Series #3 - Federated Learning for Mobile Keyboard Prediction

    Understanding how your mobile keyboard (Gboard, specifically) performs the next word prediction task and performs model training and updates

    17 min read   ·   December 27, 2021

    2021   ·   federated learning   ppml   paper-summaries   ·   PPML

    PPML Series #3 - Federated Learning for Mobile Keyboard Prediction
  • PPML Series #2 - Federated Optimization Algorithms - FedSGD and FedAvg

    A mathematical deep dive on a Federated Optimization algorithm - FedAvg and comparing it with a standard approach - FedSGD.

    16 min read   ·   December 18, 2021

    2021   ·   federated learning   ppml   paper-summaries   ·   PPML

    PPML Series #2 - Federated Optimization Algorithms - FedSGD and FedAvg
  • PPML Series #1 - An introduction to Federated Learning

    A short general introduction to Federated Learning (FL) for folks interested in privacy-preserving machine learning (PPML).

    10 min read   ·   December 11, 2021

    2021   ·   federated learning   ppml   paper-summaries   ·   PPML

    PPML Series #1 - An introduction to Federated Learning
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