✳flâneur — a map of the web's best reading
A Beginner's Guide To Understanding Convolutional Neural Networks – Adit Deshpande – Engineering at Forward | UCLA CS '19
adeshpande3.github.io · 3,918 words · saved by 1 readers
Don't worry, it's easier than it looks
A Beginner's Guide To Understanding Convolutional Neural Networks Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year's ImageNet competition (basically, the annual Olympics of computer vision), dropping the classification error record from 26% to 15%, an astounding improvement at the time.Ever since then, a host of
Explore this link on the map →saved by
related reading
- A Beginner's Guide To Understanding Convolutional Neural Networks Part 2 – Adit Deshpande – Engineering at Forward | UCLA CS '19adeshpande3.github.io
- Convolutional Neural Networks, Explained | Towards Data Sciencetowardsdatascience.com
- CS231n Deep Learning for Computer Visioncs231n.github.io
- Neural networks and deep learningneuralnetworksanddeeplearning.com
- CS231n Deep Learning for Computer Visioncs231n.github.io
- Zoom In: An Introduction to Circuitsdistill.pub
- A Recipe for Training Neural Networkskarpathy.github.io
- matlab - In Convolutional Neural Networks (CNN), how we can decide number of kernels between input and hidden layer? - Cross Validatedstats.stackexchange.com
- Feature Visualizationdistill.pub
- Computing Receptive Fields of Convolutional Neural Networksdistill.pub
- Convolution Vs Correlation | Towards Data Sciencetowardsdatascience.com
- Neural Networks, Manifolds, and Topology -- colah's blogcolah.github.io