I can't shut up about neural networks.
What questions do you have?
What questions do you have?
They aren't necessarily opposite concepts.
Fully connected refer to networks composed of layers where every node is connected to every node of the next layer.
Deep networks refer to networks with many layers. They could be fully connected or not.
Fully connected refer to networks composed of layers where every node is connected to every node of the next layer.
Deep networks refer to networks with many layers. They could be fully connected or not.
https://twitter.com/Purni80707264/status/1372641475557740552?s=20
Especially with deep learning, where you have many layers full of nodes, it's hard to understand the "thinking" of a network because you'll have to reverse-engineer million of float values and try to make sense of them.
Hard to do.
Hard to do.
https://twitter.com/mayor_onyebueke/status/1372641390136602631?s=20
Forward pass: you compute the value of every node using the current weights.
Backward propagation: You move backward through the network, re-computing the weights' value to optimize the loss of the network.
That's the way the network "learns."
Backward propagation: You move backward through the network, re-computing the weights' value to optimize the loss of the network.
That's the way the network "learns."
https://twitter.com/WaljiKazim/status/1372642051901300739?s=20
You can learn about neural networks right now with very little previous knowledge.
I'd suggest you watch @3blue1brown's videos about Neural Networks for an excellent introduction.
I'd suggest you watch @3blue1brown's videos about Neural Networks for an excellent introduction.
https://twitter.com/fwenchnotfrench/status/1372641570231492608?s=20
Backpropagation is about updating the weights to optimize our loss.
To optimize our loss we need to understand what the actual label is.
To optimize our loss we need to understand what the actual label is.
https://twitter.com/pluto_exchange/status/1372646154685591554?s=20
You experiment.
Experience gives you an idea of boundaries (why would 2,056 is stupidly large and 2 stupidly short,) but good performance comes out of a lot of experimentation.
Experience gives you an idea of boundaries (why would 2,056 is stupidly large and 2 stupidly short,) but good performance comes out of a lot of experimentation.
https://twitter.com/sharonibejih/status/1372646871861186560?s=20
Watch @3blue1brown YouTube series on neural networks for an amazing introduction.
https://twitter.com/nabilO_O/status/1372888088041639941?s=20
This thread goes over the bias and variance tradeoff:
https://twitter.com/svpino/status/1369278050970525707?s=20
https://twitter.com/Shubnam14/status/1372773747564843019?s=20
First, go through the Kaggle tutorials. Then, start with the Titanic competition.
https://twitter.com/spirosbax/status/1372806933074178050?s=20
Start with Adam.
Switch when you have a good reason to do it.
Switch when you have a good reason to do it.
https://twitter.com/GoniGerman/status/1372893477307097094?s=20
The ones that are used to solve real problems that affect humanity.
https://twitter.com/joedope/status/1372894571441577986?s=20
There's always a thread.
https://twitter.com/svpino/status/1359827674097672195?s=20
https://twitter.com/hafizkn/status/1372898359208382468?s=20
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