#### [ Neural Tangent Kernels  Learn how to compute neural tangent kernels
using torch.func  Frontend-APIs  ![](_static/img/thumbnails/cropped/generic-
pytorch-logo.png) ](intermediate/neural_tangent_kernels.html)

#### [ Performance Profiling in PyTorch  Learn how to use the PyTorch Profiler
to benchmark your module's performance.  Model-Optimization,Best-
Practice,Profiling  ![](_static/img/thumbnails/cropped/profiler.png)
](beginner/profiler.html)

#### [ Performance Profiling in TensorBoard  Learn how to use the TensorBoard
plugin to profile and analyze your model's performance.  Model-
Optimization,Best-Practice,Profiling,TensorBoard
![](_static/img/thumbnails/cropped/profiler.png)
](intermediate/tensorboard_profiler_tutorial.html)

#### [ Hyperparameter Tuning Tutorial  Learn how to use Ray Tune to find the
best performing set of hyperparameters for your model.  Model-
Optimization,Best-Practice  ![](_static/img/ray-tune.png)
](beginner/hyperparameter_tuning_tutorial.html)

#### [ Parametrizations Tutorial  Learn how to use torch.nn.utils.parametrize
to put constraints on your parameters (e.g. make them orthogonal, symmetric
positive definite, low-rank...)  Model-Optimization,Best-Practice
![](_static/img/thumbnails/cropped/parametrizations.png)
](intermediate/parametrizations.html)

#### [ Pruning Tutorial  Learn how to use torch.nn.utils.prune to sparsify
your neural networks, and how to extend it to implement your own custom
pruning technique.  Model-Optimization,Best-Practice
![](_static/img/thumbnails/cropped/Pruning-Tutorial.png)
](intermediate/pruning_tutorial.html)

#### [ How to save memory by fusing the optimizer step into the backward pass
Learn a memory-saving technique through fusing the optimizer step into the
backward pass using memory snapshots.  Model-Optimization,Best-
Practice,CUDA,Frontend-APIs  ![](_static/img/thumbnails/cropped/pytorch-
logo.png) ](intermediate/optimizer_step_in_backward_tutorial.html)

#### [ (beta) Accelerating BERT with semi-structured sparsity  Train BERT,
prune it to be 2:4 sparse, and then accelerate it to achieve 2x inference
speedups with semi-structured sparsity and torch.compile.  Text,Model-
Optimization  ![](_static/img/thumbnails/cropped/Pruning-Tutorial.png)
](advanced/semi_structured_sparse.html)

#### [ (beta) Dynamic Quantization on an LSTM Word Language Model  Apply
dynamic quantization, the easiest form of quantization, to a LSTM-based next
word prediction model.  Text,Quantization,Model-Optimization
![](_static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-an-
LSTM-Word-Language-Model.png) ](advanced/dynamic_quantization_tutorial.html)

#### [ (beta) Dynamic Quantization on BERT  Apply the dynamic quantization on
a BERT (Bidirectional Embedding Representations from Transformers) model.
Text,Quantization,Model-Optimization
![](_static/img/thumbnails/cropped/experimental-Dynamic-Quantization-on-
BERT.png) ](intermediate/dynamic_quantization_bert_tutorial.html)

#### [ (beta) Quantized Transfer Learning for Computer Vision Tutorial
Extends the Transfer Learning for Computer Vision Tutorial using a quantized
model.  Image/Video,Quantization,Model-Optimization
![](_static/img/thumbnails/cropped/60-min-blitz.png)
](intermediate/quantized_transfer_learning_tutorial.html)

#### [ (beta) Static Quantization with Eager Mode in PyTorch  This tutorial
shows how to do post-training static quantization.  Quantization
![](_static/img/thumbnails/cropped/60-min-blitz.png)
](advanced/static_quantization_tutorial.html)

#### [ Grokking PyTorch Intel CPU Performance from First Principles  A case
study on the TorchServe inference framework optimized with Intel® Extension
for PyTorch.  Model-Optimization,Production
![](_static/img/thumbnails/cropped/generic-pytorch-logo.png)
](intermediate/torchserve_with_ipex)

#### [ Grokking PyTorch Intel CPU Performance from First Principles (Part 2)
A case study on the TorchServe inference framework optimized with Intel®
Extension