Lesson 17: Deep Learning Foundations to Stable Diffusion

(All lesson resources are available at course.fast.ai.) In this lesson, we discuss the importance of weight initialization in neural networks and explore various techniques to improve training. We start by introducing changes to the miniai library and demonstrate the use of HooksCallback and ActivationStats for better visualization. We then dive into the importance of having zero mean and unit standard deviation in neural networks and introduce the Glorot (Xavier) initialization.
We also cover variance, standard deviation, and covariance, and their significance in understanding relationships between data points. We create a novel Generalized ReLU activation function and discuss the Layer-wise Sequential Unit Variance (LSUV) technique for initializing any neural network. We explore normalization techniques, such as Layer Normalization and Batch Normalization, and briefly mention other normalization methods like Instance Norm and Group Norm.
Finally, we experiment with different batch sizes, learning rates, and optimizers like Accelerated SGD, RMSProp, and Adam to improve performance.
0:00:00 - Changes to previous lesson
0:07:50 - Trying to get 90% accuracy on Fashion-MNIST
0:11:58 - Jupyter notebooks and GPU memory
0:14:59 - Autoencoder or Classifier
0:16:05 - Why do we need a mean of 0 and standard deviation of 1?
0:21:21 - What exactly do we mean by variance?
0:25:56 - Covariance
0:29:33 - Xavier Glorot initialization
0:35:27 - ReLU and Kaiming He initialization
0:36:52 - Applying an init function
0:38:59 - Learning rate finder and MomentumLearner
0:40:10 - What’s happening is in each stride-2 convolution?
0:42:32 - Normalizing input matrix
0:46:09 - 85% accuracy
0:47:30 - Using with_transform to modify input data
0:48:18 - ReLU and 0 mean
0:52:06 - Changing the activation function
0:55:09 - 87% accuracy and nice looking training graphs
0:57:16 - “All You Need Is a Good Init”: Layer-wise Sequential Unit Variance
1:03:55 - Batch Normalization, Intro
1:06:39 - Layer Normalization
1:15:47 - Batch Normalization
1:23:28 - Batch Norm, Layer Norm, Instance Norm and Group Norm
1:26:11 - Putting all together: Towards 90%
1:28:42 - Accelerated SGD
1:33:32 - Regularization
1:37:37 - Momentum
1:45:32 - Batch size
1:46:37 - RMSProp
1:51:27 - Adam: RMSProp plus Momentum
Timestamps and transcript thanks to fmussari

[@matveyshishov]

Jeremy came from some different world. Where people care. About looking under the hood and rewriting ReLU. About making sure nobody is left behind and very high quality education is free. thank you SO much, Jeremy, for being awesome!

[@aarontube]

With less than two thousand views this video is a hidden gem.

[@marsgrins]

This is my favorite lecture in part 2 so far. This stuff feels like magic.

[@jasminebhanushali8835]

This video is truly amazing, thank you so much for building this course and the great explanations!

[@coolarun3150]

awesome!!!

[@satirthapaulshyam7769]

57:40 initializing any neural network with any activation func can be done by lsuv

[@satirthapaulshyam7769]

47:40 normalizing input in transformi

[@satirthapaulshyam7769]

1:28:00 making the model fine tuned

[@satirthapaulshyam7769]

Training a fully cnn model, weight scaling by glorotxavier init(1/√n), kaiming he init 1/√(2n), simple batch normalization, still our model is not having std dev of 1 and mean of 0, Soln: leaky relu( relu is incompatible as it threshold all value under 0 to 0 so the mean will never be 0), init methods that dont fiddling around weights what we have done so far these methods basicallly changes the act values (LSUV, batch normalization, layer norm) until they have 0 and 1 mean and std also on the same time they tweak the weights and biases as we have seen in lsuv the weights are divided and biases minused and then again act values mean and stds are checked again do it in a loop, finally improved the accuracy.

[@satirthapaulshyam7769]

Leaky relu u can have a max value so that u r notgetting large values in neuron