56
edits
Changes
→Introduction to Neural Networks
[[File:Figure1.jpg]] <ref name="figure1">[https://medium.com/datadriveninvestor/infographics-digest-vol-3-da67e69d71ce]</ref>
[[File:Figure2.jpg]][https://www.analyticsvidhya.com/blog/2020/02/cnn-vs-rnn-vs-mlp-analyzing-3-types-of-neural-networks-in-deep-learning/]
[[File:Figure3.png]][https://www.youtube.com/watch?v=aircAruvnKk]
[[File:Figure4.png]][https://www.youtube.com/watch?v=aircAruvnKk]
[[File:Figure5.gif]][https://www.analyticsvidhya.com/blog/2020/02/cnn-vs-rnn-vs-mlp-analyzing-3-types-of-neural-networks-in-deep-learning/]
[[File:Figure6.png]][https://www.youtube.com/watch?v=aircAruvnKk]
=== Back Propagation ===
As you might imagine, the ANN is unlikely to get it right the first time. In fact, it will undoubtedly get it wrong, horribly wrong! It improves itself by adjusting those weights and biases mentioned earlier. In order to do this, it must be trained with tons of example numbers, as well as a cheat sheet to check its answers. How far the ANN’s final answer is from the correct answer is called the cost. Once a cost is determined, the weights and biases that make up the ANN are adjusted to minimise this cost. That’s a lot of math I summed up in one sentence. The algorithm that does this math is called Back Propagation, and it’s how an ANN learns. It’s called that because it works backwards from what it wants the output to be, down the hidden layers. This is extremely computationally intensive because it usually has tens of thousands of answers to work backwards from.[https://www.youtube.com/watch?v=Ilg3gGewQ5U&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi&index=3]
What is commonly done instead is that the training data is split into batches and the back-prop algorithm is performed on each batch. This is not as accurate as performing it on the entire training set, but is good enough for the increase in performance. As you might imagine, each batch has an opportunity to be parallelized.
Convolutional Neural Networks are faster and more accurate at image recognition than standard ANNs. They achieve this by focusing on spatial features such as ears, nose, mouth, and by ignoring irrelevant data.
[[File:Figure7.gif]][https://www.analyticsvidhya.com/blog/2020/02/cnn-vs-rnn-vs-mlp-analyzing-3-types-of-neural-networks-in-deep-learning/]
== Implementation of a Neural Network ==
