Explore >> Select a destination
|
You are here 
|
www.3blue1brown.com | ||
| | | | |
kavita-ganesan.com
|
|
| | | | | This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn. | |
| | | | |
michael-lewis.com
|
|
| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
| | | | |
www.v7labs.com
|
|
| | | | | A neural network activation function is a function that is applied to the output of a neuron. Learn about different types of activation functions and how they work. | |
| | | | |
programmathically.com
|
|
| | | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] | ||
