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ml-workshop-3-of-4

Advanced Machine Learning with Scikit-learn part I

ml-workshop-4-of-4

Advanced Machine Learning with Scikit-learn part II

ml_sagemaker_studies

Case studies, examples, and exercises for learning to deploy ML models using AWS SageMaker.

model_interpret

Interpreting Deep Learning Models Understanding output using GradCAMs Feature Importance in Machine Learning Permutation Method SHAP Values

navigation-agent

Use Deep Q Network to train a navigation agent

network-analysis-made-simple

For PyCon, PyData, ODSC, and beyond!

nlp_medicine

Question Answering with BERT, Extracting disease labels from clinical reports

odsc_west_2019

Missing data in supervised ML

openai-gym

Train a Deep Q Network to play in openAI gym environments

painting-classification

Classifier of paintings depending on their style (impressionism, barocco, ...)

plagiarism_detector

Pytorch model to detect plagiarism and deploy on AWS

pneumonia

Pneumonia detection from Chest X ray

pulse_rate

Pulse Rate Algorithm with PPG data

pydata_nyc2018-intro-to-model-interpretability

Notebook and slides for my talk at Pydata NYC 2018

pytorch-gan

PyTorch implementations of Generative Adversarial Networks.

pytorch-mask-rcnn

recommedation-engine

Recommendation engine using collaborative filtering to recommend articles

risk_model

Risk Models Using Tree-based Models

sagemaker-deployment

Code and associated files for the deploying ML models within AWS SageMaker

segmentation_ad

alzheimer disease segmentation

slam

Landmark detection and tracking

survival_models

Cox Proportional Hazards and Random Survival Forests

taxi-v2

Reinforcement learning to teach a taxi agent to navigate a small gridworld

treatment_effect

Estimating Treatment Effect Using T Learner

tumour_mri

Brain Tumor Auto-Segmentation for Magnetic Resonance Imaging (MRI)

udacity-finetuning

vui

Voice User Interface using deep learning

wgan-gp

Wasserstein GAN with Gradient Penalty