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01 - Deep Learning with PyTorch: A 60 Minute Blitz
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02 - Learning PyTorch with Examples
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03 - What is torch.nn really?
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04 - Visualizing Models, Data, and Training with TensorBoard
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05 - Writing Custom Datasets, DataLoaders and Transforms
- 01 - Introduction to Pytorch Lightning
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01 - Torchvision Object Detection Finetuning Tutorial
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02 - Transfer Learning for Computer Vision
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03 - Adversarial Example Generation
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04 - Generative Adversarial Nets
- GANs in PyTorch
- GANs in Keras
- DCGAN Tutorial
- Conditional GAN in Keras
- Wasserstein GAN in PyTorch
- Video Generation with TGAN
- ProteinGAN: Expanding Functional Protein Sequence Space Using Generative Adversarial Networks
- SN-GAN: Spectrally Normalized Generative Adversarial Networks
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
- Pix2PixHD: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- SRGAN: Super-Resolution GAN
- GauGAN: Semantic Image Synthesis with Spatially-Adaptive Normalization
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06 - Domain Adaptation Tutorial
- Introduction to Few-Shot Learning and Meta Learning
- Siamese Networks Tutorial (PyTorch)
- Siamese Networks Tutorial (Keras)
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07 - Advances in Few-Shot Learning
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08 - Meta-Learning Models
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09 - Modules for Convolutional Neural Nets
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01 - Sequence-to-Sequence Modeling with nn.Transformer and TorchText
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02 - NLP From Scratch: Classifying Names with a Character-Level RNN
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03 - NLP From Scratch: Generating Names with a Character-Level RNN
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04 - NLP From Scratch: Translation with a Sequence to Sequence Network and Attention
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05 - Text Classification with TorchText
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06 - Language Translation with TorchText
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07 - Transformers
- 01 - TorchAudio Tutorial
- 01 - Reinforcement Learning (DQN) Tutorial
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01 - Pruning Tutorial
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02 - (Experimental) Dynamic Quantization on an LSTM Word Language Model
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03 - (Experimental) Dynamic Quantization on BERT
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04 - (Experimental) Static Quantization with Eager Mode in PyTorch
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05 - (Experimental) Quantized Transfer Learning for Computer Vision Tutorial
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01 - Single-Machine Model Parallel Best Practices
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02 - Getting Started with Distributed Data Parallel
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03 - Writing Distributed Applications with PyTorch
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04 - Getting Started with Distributed RPC Framework
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05 - (Advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS
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06 - Implementing a Parameter Server Using Distributed RPC Framework