subratac

subratac's Stars

• rasbt/deeplearning-models

  A collection of various deep learning architectures, models, and tips

  Language:Jupyter Notebook16.8k4k

• DigITs-AIML/MMNN_STS

  Repository supporting the training and evaluation of a multimodal neural network for prognostic modeling of soft tissue sarcoma patient outcomes

  Language:Python2

• AI-in-Health/MedLLMsPracticalGuide

  A curated list of practical guide resources of Medical LLMs (Medical LLMs Tree, Tables, and Papers)

  1.2k106

• snap-stanford/plato

  Language:Python662

• subratac/matplotlib-gallery

  Examples of matplotlib codes and plots

  1

• ozan-oktay/Attention-Gated-Networks

  Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

  Language:Python1.9k425

• subratac/deepsearch-toolkit

  Interact with the DeepSearch platform for new knowledge explorations and discoveries - Peter Starr IBM

  1

• subratac/ML-Course-Notes

  🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.

  1

• subratac/Andrew-NG-Notes

  This is Andrew NG Coursera Handwritten Notes.

  1

• subratac/benchmark_VAE

  Unifying Generative Autoencoder implementations in Python

  1

• subratac/Robyn-MMM-from-Facebook

  Robyn is an experimental, automated and open-sourced Marketing Mix Modeling (MMM) package from Facebook Marketing Science. It uses various machine learning techniques (Ridge regression, multi-objective evolutionary algorithm for hyperparameter optimisation, gradient-based optimisation for budget allocation etc.) to define media channel efficiency and effectivity, explore adstock rates and saturation curves. It's built for granular datasets with many independent variables and therefore especially suitable for digital and direct response advertisers with rich dataset.

  1

• subratac/Bayesian-Marketing-Mix-modeling

  LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

  2

• subratac/Algorithms-in-Python

  All Algorithms implemented in Python

  1

• subratac/machine-learning-interview

  Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

  1

• subratac/semantic-segmentation

  Nvidia Semantic Segmentation monorepo

  1

• subratac/pytorch-image-models

  PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

  1

• subratac/adpkd-segmentation-pytorch

  Segmentation of kidneys on MRI in Autosomal Dominant Polycystic Kidney

  1

• subratac/applied-ml

  📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

  1

• subratac/CheXbert

  Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT

  1

• subratac/CheXseg

  Code used in the paper "CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation"

  1

• subratac/1804_python_healthcare

  pdf, py, and jupyter notebook files for https://pythonhealthcare.org/

  1

• subratac/VisualCheXbert

  Addressing the Discrepancy Between Radiology Report Labels and Image Labels

  1