import torch
import torch.nn as nn
class DataScientist(nn.Module):
def __init__(self):
super().__init__()
self.name = "Jordan Deklerk"
self.role = "Senior Data Scientist"
self.company = "DICK's Sporting Goods"
self.experience = ["Retail", "Healthcare"]
self.programming = ["Python", "R", "SQL", "SAS", "STATA"]
self.tools = ["Azure ML", "AWS Sagemaker", "Databricks", "Spark", "Docker", "Kubeflow", "GCP"]
def say_hi(self):
print("Thanks for dropping by, hope you find some of my work interesting.")
me = DataScientist()
me.say_hi()-
🔭 I’m currently working on Bayesian Media Mix Modeling
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📫 How to reach me jordan.deklerk@gmail.com
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📄 Learn about my experiences Resume
- An Introduction to Reinforcement Learning
- Masked Token Learning for Inpatient Diagnosis and Procedure Prediction
- Closing the Amortization Gap in Bayesian Deep Generative Models
- Fine-Tuning a Coding LLM for SQL Code Generation
