Explore >> Select a destination
|
You are here 
|
www.bmc.com | ||
| | | | |
www.paepper.com
|
|
| | | | | Recent advances in training deep neural networks have led to a whole bunch of impressive machine learning models which are able to tackle a very diverse range of tasks. When you are developing such a model, one of the notable downsides is that it is considered a "black-box" approach in the sense that your model learns from data you feed it, but you don't really know what is going on inside the model. | |
| | | | |
cprimozic.net
|
|
| | | | | Introduces a browser-based sandbox for building, training, visualizing, and experimenting with neural networks. Includes background information on the tool, usage information, technical implementation details, and a collection of observations and findings from using it myself. | |
| | | | |
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. | |
| | | | |
wtfleming.github.io
|
|
| | | |||
