Explore >> Select a destination
|
You are here 
|
programminghistorian.org | ||
| | | | |
natureofcode.com
|
|
| | | | | I began with inanimate objects living in a world of forces, and I gave them desires, autonomy, and the ability to take action according to a system of | |
| | | | |
neuralnetworksanddeeplearning.com
|
|
| | | | | [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens... | |
| | | | |
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. | |
| | | | |
www.superannotate.com
|
|
| | | Why use an activation function and how to choose the right one to train a neural network? Get answers to these questions and more in this post. | ||
