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Links from my talk: Life of an ML Model

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# Life Of A Model

Links from my talk: Life of an ML Model

## Introduction

1. [Prosthetic Knowledge Twitter Account](https://twitter.com/prostheticknowl)
2. [Andrew Ng's Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/)
3. [Stanford's Intro to Robotics Course](https://see.stanford.edu/Course/CS223A)

# Models

## Hybrid Algorithms
1. [Improving RANSAC-Based Segmentation Through CNN Encapsulation](https://ieeexplore.ieee.org/document/8099768)
2. [EE263: Introduction to Linear Dynamical Systems](https://see.stanford.edu/Course/EE263)

## Divide And Conquer
1. [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497?context=cs.CV)

## Visualization
1. [The dumb reason your fancy Computer Vision app isn’t working: Exif Orientation
](https://medium.com/@ageitgey/the-dumb-reason-your-fancy-computer-vision-app-isnt-working-exif-orientation-73166c7d39da)

## Model Calibration
1. [On Calibration of Modern Neural Networks](https://arxiv.org/abs/1706.04599)

## Interpretability
1. [Interpretable Neural Networks](https://towardsdatascience.com/interpretable-neural-networks-45ac8aa91411)

# Gathering Data

##  Using an Existing Dataset
1. [Google Dataset Search Tool](https://toolbox.google.com/datasetsearch)

## Collecting Your Own Data

### Gamify
1. [Quick, Draw!](https://quickdraw.withgoogle.com/)

### Synthetic Data
1. [Fake it till you make it.](https://www.intel.ai/fake-it-till-you-make-it-synthetic-datasets-assisting-machine-learning-in-data-scarce-environments/#gs.ma2m83)
2. [Learning from Simulated and Unsupervised Images through Adversarial Training](https://arxiv.org/pdf/1612.07828.pdf)

### Active Learning
1. [Datacamp Guide](https://www.datacamp.com/community/tutorials/active-learning)
2. [Large-scale interactive object segmentation with human annotators](https://arxiv.org/pdf/1903.10830.pdf)
3. [We don’t need no bounding-boxes: Training object class detectors using only human verification](https://arxiv.org/pdf/1602.08405.pdf)
4. [Revisiting knowledge transfer for training object class detectors](https://arxiv.org/pdf/1708.06128.pdf)

### Unsupervised Learning
1. [AlphaGo Zero Starting From Scratch](https://deepmind.com/blog/article/alphago-zero-starting-scratch)
2. [Learning Features By Watching Objects Move](https://arxiv.org/abs/1612.06370)

## Adapting An Existing Dataset
1. [Weakly Supervised Instance Segmentation using Class Peak Response](https://arxiv.org/pdf/1804.00880v1.pdf)

## Multimodal Data
1. [Deep Audio-Visual Speech Recognition](https://arxiv.org/pdf/1809.02108.pdf)

## Data Visualization
1. [Sentiment Classification Visualization](https://s3-us-west-1.amazonaws.com/venec/map.html#)

## Large scale data processing.
1. [Apache Beam](https://beam.apache.org/)

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