pgalko Goto Github PK
Name: pgalko
Type: User
Bio: If knowledge is power, knowing what we don’t know is wisdom
Twitter: pgalko
Location: Melbourne, Australia
pgalko's Projects
aerobic_threshold_estimation_from_dfa_alpha1
A Python script that estimates aerobic threshold HR from activity HRV data. The script iterates through a list of activities and computes DFA Alpha 1 for each, then computes HR from alpha 1 (0.75) and plots the results. Takes a while to run.
athlete_data_warehouse
A tool for bulk download, formatting and SQL DB storage of exercise, training, nutrition, wellness and health data from various trackers.
awesome-ai-agents
A list of AI autonomous agents
bambooai
A lightweight library that leverages Language Models (LLMs) to enable natural language interactions, allowing you to source and converse with data.
chatgpt_system_prompt
A collection of GPT system prompts and various prompt injection/leaking knowledge.
garminexport
Garmin Connect activity exporter and backup tool
kona-2022
Public dataset from Gustav Iden and Kristian Blummenfelt from the 2022 Ironman world championship in Kona.
ml_next_day_rmssd_prediction
Jupyter Notebook - Evaluating a Use of Regression Machine Learning Models for Predicting Morning RMSSD
ml_rest_hr_prediction_transformer
Jupiter Notebook - Predicting Resting HR timeseries using transformer model
ml_running_efficiency_predict
Jupyter Notebook - Optimizing Running Performance and Efficiency with Neural Network Models
next-number-prediction
This is an experiment evaluating the performance of GPT-3.5-Turbo compared to a simple persistence model in predicting the number that follows a sequence of numbers..
next-number-prediction-2
This is an experiment evaluating the performance of GPT-3.5-Turbo in predicting whether the next number in a sequence will be higher, lower, or equal to the last number.
query_fit_data_using_nl
This experimental project harnesses the OpenAI API to enable coaches and athletes to query data from FIT files stored in an SQL database using natural language, while also integrating relevant research papers for added context. The goal is to create a more accessible and user-friendly interface for analyzing and understanding training data.
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