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Created about 4 years ago
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JanMarcelKezmann

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This Repository contains Notes in form of Jupyter Notebooks for every Chapter of the Book "Building Machine Learning Pipelines" by Hannes Hapke & Catherine Nelson

TensorFlow-Advanced-Segmentation-Models

A Python Library for High-Level Semantic Segmentation Models based on TensorFlow and Keras with pretrained backbones.

Python

149

Semi-Supervised-Learning-Image-Classification

This library contains Semi-Supervised Learning Algorithms for Computer Vision tasks implemented with TensorFlow 2.x and Python 3.x

Python

Google-Colab-Pro-Vs-MacBook-Pro-M1-Max-24-Core

A speed comparison between the GPUs offered by Google Colab vs the MacBook M1 Max 24 Core chip

Jupyter Notebook

Apache-Airflow-Beam-TensorFlow-Examples

Various examples for TensorFlow Extended using Apache Beam and Airflow to create End2End Pipelines for Machine Learning.

Python

Flask-AI-WebApp

Keras & Flask AI Web Application

HTML

MetaDetect3D

MetaDetect3D is a post-processing tool for 3D object detection neural networks that improve calibration, performance and can be used for efficient label error detection as well.

Python

Fast-Fourier-Transformation-with-Python-and-Numpy

Jupyter Notebook

OpenAI-API-Guide

This repository contains multiple Jupyter Notebooks providing a step-by-step guide for the OpenAI APIs.

Jupyter Notebook

python-flask-firebase-web-application

Python

Boston-House-Prices

Jupyter Notebook

stockprice-analysis-and-predictions

Jupyter Notebook

Residual-Network-Architectures

Implementation of different Residual Network Architectures

Jupyter Notebook

JanMarcelKezmann/Building-ML-Pipelines-Notes

JanMarcelKezmann

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