er_tutorial.html)
  * [ Optimizing Vision Transformer Model for Deployment ](beginner/vt_tutorial.html)
  * [ Whole Slide Image Classification Using PyTorch and TIAToolbox ](intermediate/tiatoolbox_tutorial.html)

Audio

  * [ Audio I/O ](beginner/audio_io_tutorial.html)
  * [ Audio Resampling ](beginner/audio_resampling_tutorial.html)
  * [ Audio Data Augmentation ](beginner/audio_data_augmentation_tutorial.html)
  * [ Audio Feature Extractions ](beginner/audio_feature_extractions_tutorial.html)
  * [ Audio Feature Augmentation ](beginner/audio_feature_augmentation_tutorial.html)
  * [ Audio Datasets ](beginner/audio_datasets_tutorial.html)
  * [ Speech Recognition with Wav2Vec2 ](intermediate/speech_recognition_pipeline_tutorial.html)
  * [ Text-to-speech with Tacotron2 ](intermediate/text_to_speech_with_torchaudio.html)
  * [ Forced Alignment with Wav2Vec2 ](intermediate/forced_alignment_with_torchaudio_tutorial.html)

Text

  * [ Fast Transformer Inference with Better Transformer ](beginner/bettertransformer_tutorial.html)
  * [ NLP From Scratch: Classifying Names with a Character-Level RNN ](intermediate/char_rnn_classification_tutorial.html)
  * [ NLP From Scratch: Generating Names with a Character-Level RNN ](intermediate/char_rnn_generation_tutorial.html)
  * [ NLP From Scratch: Translation with a Sequence to Sequence Network and Attention ](intermediate/seq2seq_translation_tutorial.html)
  * [ Text classification with the torchtext library ](beginner/text_sentiment_ngrams_tutorial.html)
  * [ Language Translation with ` nn.Transformer  ` and torchtext ](beginner/translation_transformer.html)
  * [ Preprocess custom text dataset using Torchtext ](beginner/torchtext_custom_dataset_tutorial.html)

Backends

  * [ Introduction to ONNX ](beginner/onnx/intro_onnx.html)

Reinforcement Learning

  * [ Reinforcement Learning (DQN) Tutorial ](intermediate/reinforcement_q_learning.html)
  * [ Reinforcement Learning (PPO) with TorchRL Tutorial ](intermediate/reinforcement_ppo.html)
  * [ Train a Mario-playing RL Agent ](intermediate/mario_rl_tutorial.html)
  * [ Pendulum: Writing your environment and transforms with TorchRL ](advanced/pendulum.html)

Deploying PyTorch Models in Production

  * [ Introduction to ONNX ](beginner/onnx/intro_onnx.html)
  * [ Deploying PyTorch in Python via a REST API with Flask ](intermediate/flask_rest_api_tutorial.html)
  * [ Introduction to TorchScript ](beginner/Intro_to_TorchScript_tutorial.html)
  * [ Loading a TorchScript Model in C++ ](advanced/cpp_export.html)
  * [ (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime ](advanced/super_resolution_with_onnxruntime.html)
  * [ Real Time Inference on Raspberry Pi 4 (30 fps!) ](intermediate/realtime_rpi.html)

Profiling PyTorch

  * [ Profiling your PyTorch Module ](beginner/profiler.html)
  * [ Introduction to Holistic Trace Analysis ](beginner/hta_intro_tutorial.html)
  * [ Trace Diff using Holistic Trace Analysis ](beginner/hta_trace_diff_tutorial.html)

Code Transforms with FX

  * [ (beta) Building a Convolution/Batch Norm fuser in FX ](intermediate/fx_conv_bn_fuser.html)
  * [ (beta) Building a Simple CPU Performance Profiler with FX ](intermediate/fx_profiling_tutorial.html)

Frontend APIs

  * [ (beta) Channels Last Memory Format in PyTorch ](intermediate/memory_format_tutorial.html)
  * [ Forward-mode Automatic Differentiation (Beta) ](intermediate/forward_ad_usage.html)
  * [ Jacobians, Hessians, hvp, vhp, and more: composing function transforms ](intermediate/jacobians_hessians.html)
  * [ Model ensembling ](intermediate/ensembling.html)
  * [ Per-sample-gradients ](intermediate/per_sample_grads.html)
  * [ Using the PyTorch C++ Frontend ](advanced/cpp_frontend.html)
  * [ Dynamic Parallelism in TorchScript ](advanced/torch-script-parallelism.html)
  * [ Autograd in C++ Frontend ](advanced/cpp_autograd.html)

Extending PyTorch

  * [ Double Backward with Custom Functions ](intermediate/custom_function_dou