Pinned Repositories
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.
1804_python_healthcare
pdf, py, and jupyter notebook files for https://pythonhealthcare.org/
adpkd-segmentation-pytorch
Segmentation of kidneys on MRI in Autosomal Dominant Polycystic Kidney
Algorithms-in-Python
All Algorithms implemented in Python
Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
benchmark_VAE
Unifying Generative Autoencoder implementations in Python
CheXbert
Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT
CheXseg
Code used in the paper "CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation"
deepsearch-toolkit
Interact with the DeepSearch platform for new knowledge explorations and discoveries - Peter Starr IBM
subratac's Repositories
subratac/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
subratac/nbdev-hello-world
subratac/pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
subratac/deepad-from-Cloudera-FF-labs
Deep Learning for Anomaly Deteection
subratac/deepsearch-toolkit
Interact with the DeepSearch platform for new knowledge explorations and discoveries - Peter Starr IBM
subratac/ML-Course-Notes
🎓 Sharing course notes on all topics related to machine learning, NLP, and AI.
subratac/benchmark_VAE
Unifying Generative Autoencoder implementations in Python
subratac/Algorithms-in-Python
All Algorithms implemented in Python
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.
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.
subratac/engineering-class
Lightning Bits: Engineering for Researchers repo
subratac/coursera-deep-learning-specialization
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
subratac/git-tutorial1
subratac/machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
subratac/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
subratac/DeepLIIF
Deep Learning Inferred Multiplex ImmunoFluorescence for IHC Image Quantification (https://deepliif.org) [Nature Machine Intelligence'22]
subratac/adpkd-segmentation-pytorch
Segmentation of kidneys on MRI in Autosomal Dominant Polycystic Kidney
subratac/Andrew-NG-Notes
This is Andrew NG Coursera Handwritten Notes.
subratac/awesome-fairness-in-ai
A curated list of awesome Fairness in AI resources
subratac/kaggle-solutions
🏅 Collection of Kaggle Solutions and Ideas 🏅
subratac/annotated-transformer
http://nlp.seas.harvard.edu/2018/04/03/attention.html
subratac/MLQuestions
Machine Learning and Computer Vision Engineer - Technical Interview Questions
subratac/Grokking-Machine-Learning
This repo aims to contain different machine learning use cases along with the descriptions to the model architectures
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
subratac/pycox
Survival analysis with PyTorch
subratac/haystack
:mag: End-to-end Python framework for building natural language search interfaces to data. Leverages Transformers and the State-of-the-Art of NLP. Supports DPR, Elasticsearch, Hugging Face’s Hub, and much more!
subratac/MITx_6.86x
Notes of MITx 6.86x - Machine Learning with Python: from Linear Models to Deep Learning
subratac/FARM
:house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
subratac/bertviz
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
subratac/interpret
Fit interpretable models. Explain blackbox machine learning.