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Image Model - Teachable Machines
Train a computer to recognize your own images, sounds, & poses.
A fast, easy way to create machine learning models for your sites, apps, and more – no expertise or coding required.
ai  images  machinelearning 
11 days ago by cothrun
[D] What are some of the most impressive Deep Learning websites you've encountered? : MachineLearning
Hey all, So I've been looking towards showcasing DL to a non-technical group in my company and I would like to hear your suggestions for websites...
machinelearning  deeplearning  lists  discussion 
6 weeks ago by cothrun
Making Anime Faces With StyleGAN - Gwern.net
A tutorial explaining how to train and generate high-quality anime faces with StyleGAN neural networks, and tips/scripts for effective StyleGAN use.
ai  generators  gwern  images  machinelearning  deeplearning  neuralnetwork 
11 weeks ago by cothrun
GitHub - avinashpaliwal/Super-SloMo: PyTorch implementation of Super SloMo by Jiang et al.
PyTorch implementation of Super SloMo by Jiang et al. - avinashpaliwal/Super-SloMo
video  machinelearning  ai  slomo  pytorch 
december 2019 by cothrun
Introduction to Motion Estimation with Optical Flow
In this tutorial, we will learn what Optical Flow is, how to implement its two main variants (sparse and dense), and also get a big picture of more recent approaches involving deep learning and promising future directions.
video  machinelearning  deeplearning 
may 2019 by cothrun
Principal component analysis: pictures, code and proofs | Joel Laity
The code used to generate the plots for this post can be found here.
pca  datascience  machinelearning 
march 2019 by cothrun
5agado/data-science-learning: Repository of code and resources related to different data science and machine learning topics. For learning, practice and teaching purposes.
Repository of code and resources related to different data science and machine learning topics. For learning, practice and teaching purposes. - 5agado/data-science-learning
data  deeplearning  machinelearning 
february 2019 by cothrun
Towards Data Science
Sharing concepts, ideas, and codes.
machinelearning  blog  ai  datascience 
january 2019 by cothrun
Bias detectives: the researchers striving to make algorithms fair
As machine learning infiltrates society, scientists are trying to help ward off injustice.
machinelearning  ai  bias  datascience 
november 2018 by cothrun
algofairness/BlackBoxAuditing: Research code for auditing and exploring black box machine-learning models.
This repository contains a sample implementation of Gradient Feature Auditing (GFA) meant to be generalizable to most datasets. For more information on the repair process, see our paper on Certifying and Removing Disparate Impact. For information on the full auditing process, see our paper on Auditing Black-box Models for Indirect Influence.
machinelearning  ai  datascience  bias 
november 2018 by cothrun
adebayoj/fairml
FairML is a python toolbox auditing the machine learning models for bias.
machinelearning  ai  datascience  bias 
november 2018 by cothrun
Aequitas – Center for Data Science and Public Policy
An open source bias audit toolkit for machine learning developers, analysts, and policymakers to audit machine learning models for discrimination and bias, and make informed and equitable decisions around developing and deploying predictive risk-assessment tools.
machinelearning  statistics  bias  datascience  ai 
november 2018 by cothrun
Machine Learning for Drummers
TL;DR: In this post, I build an app that classifies whether an audio sample is a kick drum, snare drum, or other drum sample with 87% accuracy using 🎉machine learning.
machinelearning  music  python  drums 
august 2018 by cothrun
Fast.ai - Part 1 - Lesson 1 - Annotated notes
The first lesson gives an introduction into the why and how of the fast.ai course, and you will learn the basics of Jupyter Notebooks and how to use the fast.ai library to build a world-class image classifier in three lines of Python.

You will get a feel for what deep learning is and why it works, as well as possible applications you can build yourself.
ai  deeplearning  machinelearning 
july 2018 by cothrun
Seedbank
Each seed is a machine learning example you can start playing with. Explore, learn and grow them into whatever you like.
machinelearning  ai  notebook  google 
july 2018 by cothrun
higgsfield/RL-Adventure-2: PyTorch0.4 implementation of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
PyTorch tutorial of: actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
pytorch  machinelearning 
june 2018 by cothrun
Shogun Machine Learning - Home
Shogun is and open-source machine learning library that offers a wide range of efficient and unified machine learning methods.
python  c#  c++  machinelearning  ai  bayes 
april 2018 by cothrun
Pyro
Beneath the built-in inference algorithms, Pyro has a library of flexible primitives for creating new inference algorithms and working with probabilistic programs.
python  machinelearning 
november 2017 by cothrun
The Tensor Algebra Compiler (taco)
A fast and versatile library for linear and tensor algebra
machinelearning  linearalgebra  library  compiler 
november 2017 by cothrun
The limitations of deep learning
The limitations of deep learning
Mon 17 July 2017
By Francois Chollet
In Essays.
This post is adapted from Section 2 of Chapter 9 of my book, Deep Learning with Python (Manning Publications). It is part of a series of two posts on the current limitations of deep learning, and its future.

This post is targeted at people who already have significant experience with deep learning (e.g. people who have read chapters 1 through 8 of the book). We assume a lot of pre-existing knowledge.

Deep learning: the geometric view

The most surprising thing about deep learning is how simple it is. Ten years ago, no one expected that we would achieve such amazing results on machine perception problems by using simple parametric models trained with gradient descent. Now, it turns out that all you need is sufficiently large parametric models trained with gradient descent on sufficiently many examples. As Feynman once said about the universe, "It's not complicated, it's just a lot of it".
machinelearning  deeplearning  ai 
july 2017 by cothrun
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