User-Friendly Interface
Deep Playground offers a visually intuitive and easy-to-use interface for experimenting with neural networks, making it accessible to beginners.
Real-Time Visualization
It provides real-time visualization of how neural networks adjust during training, which helps in understanding the learned representations and model behavior.
Interactive Learning
Users can interactively change parameters like learning rate, activation functions, and neurons, facilitating a hands-on learning experience about neural networks.
Educational Tool
The platform is specifically designed as an educational tool to help users grasp fundamental machine learning concepts without requiring a complex setup.
We have collected here some useful links to help you find out if Deep playground is good.
Check the traffic stats of Deep playground on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Deep playground on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Deep playground's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Deep playground on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Deep playground on Reddit. This can help you find out how popualr the product is and what people think about it.
A neural network is essentially an algorithm that uses weights and activation functions, which allow it to recognise patterns in the most complicated data. Try it out here! - Source: dev.to / about 1 year ago
I did a research project on this a while back - and when it comes to understanding deep network learning rate, regularization, hidden layer effects, and activations, I don't think anything is better than [this little web... - Source: Hacker News / almost 2 years ago
Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good. For example, take the circle dataset here: https://playground.tensorflow.org That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by... - Source: Hacker News / over 2 years ago
For visualisation and some fun: http://playground.tensorflow.org/. - Source: dev.to / over 2 years ago
Https://seeing-theory.brown.edu/ https://www.3blue1brown.com/ https://playground.tensorflow.org/. - Source: Hacker News / almost 3 years ago
Thereโs an interactive neural network you can train here, which can give some intuition on wider vs larger networks: https://mlu-explain.github.io/neural-networks/ See also here: http://playground.tensorflow.org/. - Source: Hacker News / about 3 years ago
This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a โclassifierโ, to try to classify data points into whatever category they best belong in. Source: about 3 years ago
I donโt know much experimenting youโve done, but many repeated small scale experiments might give you a better intuition at least. I highly recommend this online tool for playing with different environmental variables, even if youโre comfortable coding up your own experiments: http://playground.tensorflow.org. Source: about 3 years ago
Even if you canโt code, play around with this tool: https://playground.tensorflow.org โ you can adjust the shape of the NN and watch how well it classifies the data. Model size obviously matters. Source: about 3 years ago
I don't think so. You can easily play around in the browser, using Javascript, or on https://processing.org/, https://playground.tensorflow.org/, https://scratch.mit.edu/, etc. If anything the problem is that today's kids have too many options. And sure, some are commercial. - Source: Hacker News / about 3 years ago
Well there is no point of using a multilayer linear neural network, because a cascade of linear transformations can be reduced to a single linear transformation. So you can only approximate linear functions. However if you have prior knowledge about the non linearity of your data lets say you know that it is a linear combination of polynomials up to certain degree, you can expand your input space by explicitly... Source: over 3 years ago
We do understand how it works. Basically we feed lots of training data into a very big matrix of numbers and then optimize the numbers using well known calculus. It's easy to prove that this approach works for learning simple Mathematical problems. See for example https://playground.tensorflow.org. Source: over 3 years ago
You can actually play with training a very simple model in your web browser here to get an idea of how that works. The important part though is that training is kind of a trial and error adjustment process. Source: over 3 years ago
This is pretty cool: https://playground.tensorflow.org/. Source: over 3 years ago
If you want to play with a (very simple) neural network yourself, you can go to https://playground.tensorflow.org/ ; the source code to this application is at https://github.com/tensorflow/playground. Source: over 3 years ago
Https://playground.tensorflow.org/ can be a good place to start to get some visual representation of it. Source: over 3 years ago
To really see the weight learning in realtime, I guess you are looking for something like playground but applicable to your owm models. Source: about 4 years ago
Hm, during one of my machine learning courses we learned about this website: Http://playground.tensorflow.org Itโs not exactly a fully platform, but it does help to learn about the effects of different parameters. Source: over 4 years ago
A good introduction to neural networks can be found here: https://playground.tensorflow.org A parameter is a "weight" in this case (the lines drawn from neuron to neuron). The neurons are effectively runtime values or "activations." Parameters (weights) are updated during training and then set as constant during "inference" (also called "prediction"). There's unfortunately a ton of jargon and different groups... - Source: Hacker News / over 4 years ago
Hello HN! I want to create a small animation that looks like the graph on https://playground.tensorflow.org/ (hit play to see the animation). I have a few images that I want to connect with those "animated paths". - Source: Hacker News / over 4 years ago
I thought I knew neural nets until I tried to fit the spiral on here https://playground.tensorflow.org. Source: over 4 years ago
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