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dl
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neomatrix369
commented
Nov 6, 2019
As see from #53 we can have broken/dead links, links that once worked can be unavailable for reasons outside the control of this project/repo!
Hence I have decided to manually scan (for now) the repo from time to time for such links and fix them - if there is one. Here are the steps to take:
New broken/dead links
- find missing links using,
markdown-link-check(see https://www.npmjs.c
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Oct 29, 2019 - Python
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python
zsh
opencv
deep-learning
ffmpeg
ubuntu
anaconda
tensorflow
cuda
ml
pytorch
installers
deeplearning
dl
tensorflow-setup
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DAB/DAB+ receiver for Linux (including ETI-NI and EDI AF playback)
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Lab worksheets for the Applied Deep Learning Course.
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ai
deep-learning
opendata
artificial-intelligence
medical-imaging
corona
dl
2019-ncov
2019ncov
coronavirus
2019ncov-api
covid-19
covid19
sars-cov-2
covid19-data
2019-ncov-data
coronavirus-disease
covid-net
covid19-dataset
coronavirus-fight
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May 21, 2020
This is Andrew NG Coursera Handwritten Notes.
data-science
machine-learning
deep-neural-networks
reinforcement-learning
deep-learning
neural-network
numpy
ml
coursera
pandas
coursera-machine-learning
dl
andrew-ng
andrew-ng-course
andrewng
andrew-ng-machine-learning
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Aug 29, 2019 - Jupyter Notebook
курс "Глубокое обучение (Deep Learning)" на факультете ВМК МГУ имени М.В. Ломоносова
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A python based utility to download courses from infosec4tc.teachable.com , academy.ehacking.net and stackskills.com for personal offline use.
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A Multi Layer Perceptron (MLP) Artificial Neural Network (ANN) Framework Developed in C for Machine Learning (ML) and Deep Learning (DL)
c
machine-learning
ai
deep-learning
ml
artificial-intelligence
mlp
ann
dl
multi-layer-perceptron
artificial-neural-network
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A curated list of applied machine learning and data science notebooks and libraries across different industries.
application
machine-learning
real-time
theano
deep-learning
tensorflow
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keras
ml
project
pytorch
rl
machinelearning
deeplearning
dl
real-time-data
ml-application
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Re-implements the DeepInverse
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Text to Video Generation Problem
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lihanghang / ML
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In the examples like tensorflow_mnist or scikit_learn in advisor_client, the config file has the goal MINIMIZE. In the scripts the metric used is accuracy. Am I missing something? Shouldn't the goal be maximize?