Explore >> Select a destination
|
You are here 
|
questionableengineering.com | ||
| | | | |
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.jeremymorgan.com
|
|
| | | | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | |
| | | | |
www.kdnuggets.com
|
|
| | | | | Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network. | |
| | | | |
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... | ||
