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I still can't understand why BatchNorm helps against vanishing/exploding gradients. Is there any ideas?     See Less

Thank you @Andrej for bringing this series. You are a great teacher, the way you have simplified such seemingly complex topics is valuable to all the students like me. 🙏     See Less

🎯Course outline for quick navigation:

[00:00-03:21]1.     See More ng and refactoring neural networks for language modeling
-[00:00-00:30]Continuing makemore implementation with multilayer perceptron for character-level language modeling, planning to move to larger neural networks.
-[00:31-01:03]Understanding neural net activations and gradients in training is crucial for optimizing architectures.
-[02:06-02:46]Refactored code to optimize neural net with 11,000 parameters over 200,000 steps, achieving train and val loss of 2.16.
-[03:03-03:28]Using torch.nograd decorator to prevent gradients computation.

[03:22-14:22]2. Efficiency of torch.no_grad and neural net initialization issues
-[03:22-04:00]Using torch's no_grad makes computation more efficient by eliminating gradient tracking.
-[04:22-04:50]Network initialization causes high loss of 27, rapidly decreases to 1 or 2.
-[05:00-05:32]At initialization, the model aims for a uniform distribution among 27 characters, with roughly 1/27 probability for each.
-[05:49-06:19]Neural net creates skewed probability distributions leading to high loss.
-[12:08-12:36]Loss at initialization as expected, improved to 2.12-2.16

[14:24-36:39]3. Neural network initialization
-[16:03-16:31]The chain rule with local gradient is affected when outputs of tanh are close to -1 or 1, leading to a halt in back propagation.
-[18:09-18:38]Concern over destructive gradients in flat regions of h outputs, tackled by analyzing absolute values.
-[26:03-26:31]Optimization led to improved validation loss from 2.17 to 2.10 by fixing softmax and 10-inch layer issues.
-[29:28-30:02]Standard deviation expanded to three, aiming for unit gaussian distribution in neural nets.
-[30:17-30:47]Scaling down by 0.2 shrinks gaussian with standard deviation 0.6.
-[31:03-31:46]Initializing neural network weights for well-behaved activations, kaiming he et al.
-[36:24-36:55]Modern innovations have improved network stability and behavior, including residual connections, normalization layers, and better optimizers.

[36:39-51:52]4. Neural net initialization and batch normalization
-[36:39-37:05]Modern innovations like normalization layers and better optimizers reduce the need for precise neural net initialization.
-[40:32-43:04]Batch normalization enables reliable training of deep neural nets, ensuring roughly gaussian hidden states for improved performance.
-[40:51-41:13]Batch normalization from 2015 enabled reliable training of deep neural nets.
-[41:39-42:09]Standardizing hidden states to be unit gaussian is a perfectly differentiable operation, a key insight in the paper.
-[43:20-43:50]Calculating standard deviation of activations, mean is average value of neuron's activation.
-[45:45-46:16]Back propagation guides distribution movement, adding scale and shift for final output

[51:52-01:01:35]5. Jittering and batch normalization in neural network training
-[52:10-52:37]Padding input examples adds entropy, augments data, and regularizes neural nets.
-[53:44-54:09]Batch normalization effectively controls activations and their distributions.
-[56:05-56:33]Batch normalization paper introduces running mean and standard deviation estimation during training.
-[01:00:46-01:01:10]Eliminated explicit calibration stage, almost done with batch normalization, epsilon prevents division by zero.

[01:01:36-01:09:21]6. Batch normalization and resnet in pytorch
-[01:02:00-01:02:30]Biases are subtracted out in batch normalization, reducing their impact to zero.
-[01:03:13-01:03:53]Using batch normalization to control activations in neural net, with gain, bias, mean, and standard deviation parameters.
-[01:07:25-01:07:53]Creating deep neural networks with weight layers, normalization, and non-linearity, as exemplified in the provided code.

[01:09:21-01:23:37]7. Pytorch weight initialization and batch normalization
-[01:10:05-01:10:32]Pytorch initializes weights using 1/fan-in square root from a uniform distribution.
-[01:11:11-01:11:40]Scaling weights by 1 over sqrt of fan in, using batch normalization layer in pytorch with 200 features.
-[01:14:02-01:14:35]Importance of understanding activations and gradients in neural networks, especially as they get bigger and deeper.
-[01:16:00-01:16:30]Batch normalization centers data for gaussian activations in deep neural networks.
-[01:17:32-01:18:02]Batch normalization, influential in 2015, enabled reliable training of much deeper neural nets.

[01:23:39-01:55:56]8. Custom pytorch layer and network analysis
-[01:24:01-01:24:32]Updating buffers using exponential moving average with torch.nograd context manager.
-[01:25:47-01:27:11]The model has 46,000 parameters and uses pytorch for forward and backward passes, with visualizations of forward pass activations.
-[01:28:04-01:28:30]Saturation stabilizes at 20% initially, then stabilizes at 5% with a standard deviation of 0.65 due to gain set at 5 over 3.
-[01:33:19-01:33:50]Setting gain correctly at 1 prevents shrinking and diffusion in batch normalization.
-[01:38:41-01:39:11]The last layer has gradients 100 times greater, causing faster training, but it self-corrects with longer training.
-[01:43:18-01:43:42]Monitoring update ratio for parameters to ensure efficient training, aiming for -3 on log plot.
-[01:51:36-01:52:04]Introduce batch normalization and pytorch modules for neural networks.
-[01:52:39-01:53:06]Introduction to diagnostic tools for neural network analysis.
-[01:54:45-01:55:50]Introduction to diagnostic tools in neural networks, active research in initialization and backpropagation, ongoing progress

offered by Coursnap    See Less

Amazing, knowledge that is hell hard to find in other videos and also, you have AMAZING skill in clearly explaining complex stuff.     See Less

This is a great lecture, especially the second half building intuition about diagnostics. Amazing stuff.     See Less

The amount of useful information in this video is impressive. Thanks for such good content.     See Less

I keep coming back to these videos again and again. Andrej is legend!     See Less

Thanks for the fantastic download! You have changed my learning_rate in this area from 0.1 to something >1!     See Less

what is the purpose of bnmean_running and bnstd_running?     See Less